Datasets:

debajyotidasgupta

/

dblp_2022_2023

like 0

Title: Freely Given Consent?: Studying Consent Notice of Third-Party Tracking and Its Violations of GDPR in Android Apps Abstract: ABSTRACTAdopted in May 2018, the European Union's General Data Protection Regulation (GDPR) requires the consent for processing users' personal data to be freely given, specific, informed, and unambiguous. While prior work has shown that this often is not given through automated network traffic analysis, no research has systematically studied how consent notices are currently implemented and whether they conform to GDPR in mobile apps. To close this research gap, we perform the first large-scale study into consent notices for third-party tracking in Android apps to understand the current practices and the current state of GDPR's consent violations. Specifically, we propose a mostly automated and scalable approach to identify the currently implemented consent notices and apply it to a set of 239,381 Android apps. As a result, we recognize four widely implemented mechanisms to interact with the consent user interfaces from 13,082 apps. We then develop a tool that automatically detects users' personal data sent out to the Internet with different consent conditions based on the identified mechanisms. Doing so, we find 30,160 apps do not even attempt to implement consent notices for sharing users' personal data with third-party data controllers, which mandate explicit consent under GDPR. In contrast, out of 13,082 apps implemented consent notices, we identify 2,688 (20.54%) apps violate at least one of the GDPR consent requirements, such as trying to deceive users into accepting all data sharing or even continuously transmitting data when users have explicitly opted out. To allow developers to address the problems, we send emails to notify affected developers and gather insights from their responses. Our study shows the urgent need for more transparent processing of personal data and supporting developers in this endeavor to comply with legislation, ensuring users can make free and informed choices regarding their data.

Title: NARRATOR: Secure and Practical State Continuity for Trusted Execution in the Cloud Abstract: ABSTRACTPublic cloud platforms have leveraged Trusted Execution Environment (TEE) technology to provide confidential computing services. However, TEE-protected applications still suffer from rollback or forking attacks, in which their states could be rolled back to a stale version or be forked into multiple versions, resulting in state continuity violations. Existing solutions against these attacks either rely on weak threat models based on centralized trust (e.g., trusted server) or suffer from large performance overheads (e.g., tens of state updates per second). In this paper, we propose Narrator, a secure and practical system, (1) that relies on a blockchain (i.e., decentralized trust) and TEEs, and (2) that provides high-performance state continuity protection like unlimited and fast state updates for applications in cloud TEEs. The intuition behind our design is simple. Our design uses the blockchain to initialize a distributed system of TEEs, laying down the decentralized trust base with a small interaction overhead, while the distributed system provides performant state continuity protection. Our distributed system adopts a customized version of the consistent broadcast protocol and leverages advanced techniques to make state updates processed with one round trip delay on average. We build a proof-of-concept of Narrator on Intel SGX (i.e., a representative design of TEEs) and do extensive experiments to evaluate its performance. Our evaluation results show that in a LAN environment with 5 nodes, Narrator can support about 6k state updates per second, meanwhile keeping the latency as low as 3-8 ms. The throughput is 30x larger than that in ROTE and 70x larger than using a TPM counter.

Title: Cart-ology: Intercepting Targeted Advertising via Ad Network Identity Entanglement Abstract: ABSTRACTTargeted advertising is a pervasive practice in the advertising ecosystem, with complex representations of user identity central to targeting. Ad networks are incentivized to tie ephemeral cookies across devices to lasting durable identifiers such as email addresses in order to develop comprehensive cross-device user profiles. Third-party ad networks typically do not have relationships with users and must rely on external parties such as merchant websites for durable identity information, introducing intricate trust relationships. We find attackers can exploit these trust relationships to confuse an ad network into linking an unprivileged attacker's browser to a victim's identity, thus "impersonating" the victim to the ad network. We present Advertising Identity Entanglement, a vulnerability to extract specific user browsing behavior from ad networks remotely, knowing only a victim's email address, with no access to the victim, ad network, or websites. This new fundamental flaw in cross-device tracking allows attackers to pass erroneous identity information to third-party ad networks, causing the networks to confuse attacker and victim. Once entangled, the attacker receives advertisements intended for the victim across the entire ad network. We find identity entanglement is a significant user privacy vulnerability where attackers can learn detailed victim browsing activity such as retail websites, products, and even specific apartments or hotels the victim has interacted with. The vulnerability is also bi-directional, with the attacker able to cause specific ads to be shown to the victim, introducing the possibility of embarrassment attacks and blackmail. We have disclosed the vulnerability; Criteo, one of the largest third-party ad networks, acknowledges the attack.

Title: FABEO: Fast Attribute-Based Encryption with Optimal Security Abstract: ABSTRACTAttribute-based encryption (ABE) enables fine-grained access control on encrypted data and has a large number of practical applications. This paper presents FABEO: faster pairing-based ciphertext-policy and key-policy ABE schemes that support expressive policies and put no restriction on policy type or attributes, and the first to achieve optimal, adaptive security with multiple challenge ciphertexts. We implement our schemes and demonstrate that they perform better than the state-of-the-art (Bethencourt et al. S&P 2007, Agrawal et al., CCS 2017 and Ambrona et al., CCS 2017) on all parameters of practical interest.

Title: Blazing Fast PSI from Improved OKVS and Subfield VOLE Abstract: ABSTRACTWe present new semi-honest and malicious secure PSI protocols that outperform all prior works by several times in both communication and running time. Our semi-honest protocol for n = 2^20 can be performed in 0.37 seconds compared to the previous best of 2 seconds (Kolesnikov et al., CCS 2016). This can be further reduced to 0.16 seconds with 4 threads. Similarly, our protocol sends 187n bits compared to 426n bits of the next most communication-efficient protocol (Rindal et al., Eurocrypt 2021). Additionally, we apply our new techniques to the circuit PSI protocol of Rindal et al. and observe a 6x improvement in running time. These performance results are obtained by two types of improvements. The first is an optimization to the protocol of Rindal et al. to utilize sub-field vector oblivious linear evaluation. This optimization allows our construction to be the first to achieve a communication complexity of O(n lambda + n log n) where lambda is the statistical security parameter. In particular, the communication overhead of our protocol does not scale with the computational security parameter times n. Our second improvement is to the OKVS data structure which our protocol crucially relies on. In particular, our construction improves both the computation and communication efficiency as compared to prior work (Garimella et al., Crypto 2021). These improvements stem from algorithmic changes to the data structure along with new techniques for obtaining both asymptotic and tight concrete bounds on its failure probability.

Title: Strengthening Order Preserving Encryption with Differential Privacy Abstract: ABSTRACTCiphertexts of an order-preserving encryption (OPE) scheme preserve the order of their corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this preserved order. Differential privacy (DP) has become the de-facto standard for data privacy. One of the most attractive properties of DP is that any post-processing computation, such as inference attacks, performed on the noisy output of a DP algorithm does not degrade its privacy guarantee. In this work, we propose a novel differentially private order preserving encryption scheme, OP ε. Under OP ε, the leakage of order from the ciphertexts is differentially private. Consequently, in the least, OP ε ensures a formal guarantee (a relaxed DP guarantee) even in the face of inference attacks. To the best of our knowledge, this is the first work to combine DP with a OPE. OP ε is based on a novel differentially private order preserving encoding scheme, OPεc, that can be of independent interest in the local DP setting. We demonstrate OP ε's utility in answering range queries via empirical evaluation on four real-world datasets. For instance, OP ε misses only around 4 in every 10K correct records on average for a dataset of size ~732K with an attribute of domain size ~18K and ε= 1.

Title: ROAST: Robust Asynchronous Schnorr Threshold Signatures Abstract: ABSTRACTBitcoin and other cryptocurrencies have recently introduced support for Schnorr signatures whose cleaner algebraic structure, as compared to ECDSA, allows for simpler and more practical constructions of highly demanded ''t-of-n'' threshold signatures. However, existing Schnorr threshold signature schemes still fall short of the needs of real-world applications due to their assumption that the network is synchronous and due to their lack of robustness, i.e., the guarantee that t honest signers are able to obtain a valid signature even in the presence of other malicious signers who try to disrupt the protocol. This hinders the adoption of threshold signatures in the cryptocurrency ecosystem, e.g., in second-layer protocols built on top of cryptocurrencies. In this work, we propose ROAST, a simple wrapper that turns a given threshold signature scheme into a scheme with a robust and asynchronous signing protocol, as long as the underlying signing protocol is semi-interactive (i.e., has one preprocessing round and one actual signing round), provides identifiable aborts, and is unforgeable under concurrent signing sessions. When applied to the state-of-the-art Schnorr threshold signature scheme FROST, which fulfills these requirements, we obtain a simple, efficient, and highly practical Schnorr threshold signature scheme.

Title: FeIDo: Recoverable FIDO2 Tokens Using Electronic IDs Abstract: ABSTRACTTwo-factor authentication (2FA) mitigates the security risks of passwords as sole authentication factor. FIDO2---the de facto standard for interoperable web authentication---leverages strong, hardware-backed second factors. However, practical challenges hinder wider FIDO2 user adoption for 2FA tokens, such as the extra costs (20-30 per token) or the risk of inaccessible accounts upon token loss/theft. To tackle the above challenges, we propose FeIDo, a virtual FIDO2 token that combines the security and interoperability of FIDO2 2FA authentication with the prevalence of existing eIDs (e.g., electronic passports). Our core idea is to derive FIDO2 credentials based on personally-identifying and verifiable attributes---name, date of birth, and place of birth---that we obtain from the user's eID. As these attributes do not change even for refreshed eID documents, the credentials "survive" token loss. Even though FeIDo operates on privacy-critical data, all personal data and resulting FIDO2 credentials stay unlinkable, are never leaked to third parties, and are securely managed in attestable hardware containers (e.g., SGX enclaves). In contrast to existing FIDO2 tokens, FeIDo can also derive and share verifiable meta attributes (anonymous credentials) with web services. These enable verified but pseudonymous user checks, e.g., for age verification (e.g., "is adult").

Title: Escaping the Confines of Time: Continuous Browser Extension Fingerprinting Through Ephemeral Modifications Abstract: ABSTRACTBrowser fingerprinting continues to proliferate across the web. Critically, popular fingerprinting libraries have started incorporating extension-fingerprinting capabilities, thus exacerbating the privacy loss they can induce. In this paper we propose continuous fingerprinting, a novel extension fingerprinting technique that captures a critical dimension of extensions' functionality that allowed them to elude all prior behavior-based techniques. Specifically, we find that ephemeral modifications are prevalent in the extension ecosystem, effectively rendering such extensions invisible to prior approaches that are confined to analyzing snapshots that capture a single moment in time. Accordingly, we develop Chronos, a system that captures the modifications that occur throughout an extension's life cycle, enabling it to fingerprint extensions that make transient modifications that leave no visible traces at the end of execution. Specifically, our system creates behavioral signatures that capture nodes being added to or removed from the DOM, as well as changes being made to node attributes. Our extensive experimental evaluation highlights the inherent limits of prior snapshot-based approaches, as Chronos is able to identify 11,219 unique extensions, increasing coverage by 66.9% over the state of the art. Additionally, we find that our system captures a unique modification event (i.e., mutation) for 94% of the extensions, while also being able to resolve 97% of the signature collisions across extensions that affect existing snapshot-based approaches. Our study more accurately captures the extent of the privacy threat presented by extension fingerprinting, which warrants more attention by privacy-oriented browser vendors that, up to this point, have focused on deploying countermeasures against other browser fingerprinting vectors.

Title: Bullshark: DAG BFT Protocols Made Practical Abstract: ABSTRACTWe present Bullshark, the first directed acyclic graph (DAG) based asynchronous Byzantine Atomic Broadcast protocol that is optimized for the common synchronous case. Like previous DAG-based BFT protocols [19, 25], Bullshark requires no extra communication to achieve consensus on top of building the DAG. That is, parties can totally order the vertices of the DAG by interpreting their local view of the DAG edges. Unlike other asynchronous DAG-based protocols, Bullshark provides a practical low latency fast-path that exploits synchronous periods and deprecates the need for notoriously complex view-change and view-synchronization mechanisms. Bullshark achieves this while maintaining all the desired properties of its predecessor DAG-Rider [25]. Namely, it has optimal amortized communication complexity, it provides fairness and asynchronous liveness, and safety is guaranteed even under a quantum adversary. In order to show the practicality and simplicity of our approach, we also introduce a standalone partially synchronous version of Bullshark, which we evaluate against the state of the art. The implemented protocol is embarrassingly simple (200 LOC on top of an existing DAG-based mempool implementation). It is highly efficient, achieving for example, 125,000 transactions per second with a 2 seconds latency for a deployment of 50 parties. In the same setting, the state of the art pays a steep 50% latency increase as it optimizes for asynchrony.

Title: Batching, Aggregation, and Zero-Knowledge Proofs in Bilinear Accumulators Abstract: ABSTRACTAn accumulator is a cryptographic primitive that allows a prover to succinctly commit to a set of values while being able to provide proofs of (non-)membership. A batch proof is an accumulator proof that can be used to prove (non-)membership of multiple values simultaneously. In this work, we present a zero-knowledge batch proof with constant proof size and constant verification in the Bilinear Pairings (BP) setting. Our scheme is 16x to 42x faster than state-of-the-art SNARK-based zero-knowledge batch proofs in the RSA setting. Additionally, we propose protocols that allow a prover to aggregate multiple individual non-membership proofs, in the BP setting, into a single batch proof of constant size. Our construction for aggregation satisfies a strong soundness definition - one where the accumulator value can be chosen arbitrarily. We evaluate our techniques and systematically compare them with RSA-based alternatives. Our evaluation results showcase several scenarios for which BP accumulators are clearly preferable and can serve as a guideline when choosing between the two types of accumulators.

Title: Zapper: Smart Contracts with Data and Identity Privacy Abstract: ABSTRACTPrivacy concerns prevent the adoption of smart contracts in sensitive domains incompatible with the public nature of shared ledgers. We present Zapper, a privacy-focused smart contract system allowing developers to express contracts in an intuitive frontend. Zapper hides not only the identity of its users but also the objects they access---the latter is critical to prevent deanonymization attacks. Specifically, Zapper compiles contracts to an assembly language executed by a non-interactive zero-knowledge processor and hides accessed objects by an oblivious Merkle tree construction. We implemented Zapper on an idealized ledger and evaluated it on realistic applications, showing that it allows generating new transactions within 22 s and verifying them within 0.03 s (excluding the time for consensus). This performance is in line with the smart contract system ZEXE (Bowe et al., 2020), which offers analogous data and identity privacy guarantees but suffers from multiple shortcomings affecting security and usability.

Title: VeRSA: Verifiable Registries with Efficient Client Audits from RSA Authenticated Dictionaries Abstract: ABSTRACTVerifiable registries allow clients to securely access a key-value mapping maintained by an untrusted server. Registries must be audited to ensure global invariants are preserved, which, in turn, allows for efficient monitoring of individual registry entries by their owners. To this end, existing proposals either assume trusted third-party auditors or rely on incrementally verifiable computation (IVC) via expensive recursive SNARKs to make registries client-auditable. In this work, we give new client-auditable verifiable registries with throughputs up to 100x greater than baseline IVC solutions. Our approach relies on an authenticated dictionary based on RSA accumulators for which we develop a new constant-size invariant proof. We use this as a replacement for Merkle trees to optimize the baseline IVC approach, but also provide a novel construction which dispenses with SNARKs entirely. This latter solution adopts a new checkpointing method to ensure client view consistency.

Title: Practical Volume-Hiding Encrypted Multi-Maps with Optimal Overhead and Beyond Abstract: ABSTRACTEncrypted multi-map (EMM), as a special case of structured encryption, has attracted extensive attention recently. However, most of EMM constructions reveal the real volumes of queried keys, which can be leveraged to launch leakage-abuse attacks, as demonstrated by Kellaris et al. in CCS 2016 and Kornaropoulos et al. in S&P 2021. In this paper, we propose a practical non-lossy volume-hiding EMM scheme, XorMM, that can achieve optimal query communication complexity with minimal storage cost. Specifically, compared to the state-of-the-art dprfMM (Patel et al. CCS 2019), the client in our scheme receives only ℓ matching results while not suffering from data loss, where ℓ is the maximum volume of all keys. In addition, the storage cost of XorMM is approximately 1.23n, where n is the total number of key/value pairs. In contrast, the query communication and storage complexity of dprfMM is 2ℓ and 2(1+α)n respectively, where 0 Furthermore, we initiate the study of volume-hiding EMM against malicious servers. To the best of our knowledge, we present the first verifiable volume-hiding EMM scheme, XorMM, from merely symmetric cryptographic tools. The scheme still outperforms dprfMM while supporting verifiability, the query complexity and storage overhead of which are approximately ℓ +1 and 2.46n, respectively. Finally, we implement our proposed schemes and compare them with the most efficient scheme dprfMM (Patel et al. CCS 2019). The experimental results demonstrate that both of our schemes are superior to the state-of-the-art in both search and storage cost. In particular, XorMM (resp. VXorMM) brings a saving of 76% (resp. 52%) in server storage cost and achieves a speedup of 1.8x (resp. 1.6x) in search latency.

Title: ENGRAFT: Enclave-guarded Raft on Byzantine Faulty Nodes Abstract: ABSTRACTThis paper presents the first critical analysis of building highly secure, performant, and confidential Byzantine fault-tolerant (BFT) consensus by integrating off-the-shelf crash fault-tolerant (CFT) protocols with trusted execution environments (TEEs). TEEs, like Intel SGX, are CPU extensions that offer applications a secure execution environment with strong integrity and confidentiality guarantees, by leveraging techniques like hardware-assisted isolation, memory encryption, and remote attestation. It has been speculated that when implementing a CFT protocol inside Intel SGX, one would achieve security properties similar to BFT. However, we show in this work that simply combining CFT with SGX does not directly yield a secure BFT protocol, given the wide range of attack vectors on SGX. We systematically study the fallacies in such a strawman design by performing model checking, and propose solutions to enforce safety and liveness. We also present ENGRAFT, a secure enclave-guarded Raft implementation that, firstly, achieves consensus on a cluster of 2f+1 machines tolerating up to f nodes exhibiting Byzantine-fault behavior (but well-behaved enclaves); secondly, offers a new abstraction of confidential consensus for privacy-preserving state machine replication; and finally, allows the reuse of a production-quality Raft implementation, BRaft, in the development of a highly performant BFT system.

Title: Group Property Inference Attacks Against Graph Neural Networks Abstract: ABSTRACTRecent research has shown that machine learning (ML) models are vulnerable to privacy attacks that leak information about the training data. In this work, we consider Graph Neural Networks (GNNs) as the target model, and focus on a particular type of privacy attack named property inference attack (PIA) which infers the sensitive properties of the training graph through the access to GNNs. While the existing work has investigated PIAs against graph-level properties (e.g., node degree and graph density), we are the first to perform a systematic study of the group property inference attacks (GPIAs) that infer the distribution of particular groups of nodes and links (e.g., there are more links between male nodes than those between female nodes) in the training graph. First, we consider a taxonomy of threat models with various types of adversary knowledge, and design six different attacks for these settings. Second, we demonstrate the effectiveness of these attacks through extensive experiments on three representative GNN models and three real-world graphs. Third, we analyze the underlying factors that contribute to GPIA's success, and show that the GNN model trained on the graphs with or without the target property represents some dissimilarity in model parameters and/or model outputs, which enables the adversary to infer the existence of the property. Further, we design a set of defense mechanisms against the GPIA attacks, and demonstrate empirically that these mechanisms can reduce attack accuracy effectively with small loss on GNN model accuracy.

Title: AntMan: Interactive Zero-Knowledge Proofs with Sublinear Communication Abstract: ABSTRACTRecent works on interactive zero-knowledge (ZK) protocols provide a new paradigm with high efficiency and scalability. However, these protocols suffer from high communication overhead, often linear to the circuit size. In this paper, we proposed two new ZK protocols with communication sublinear to the circuit size, while maintaining a similar level of computational efficiency. (1) We designed a ZK protocol that can prove B executions of any circuit C in communication O(B + |C|) field elements (with free addition gates), while the best prior work requires a communication of O(B|C|) field elements. Our protocol is enabled by a new tool called as information-theoretic polynomial authentication code, which may be of independent interest. (2) We developed an optimized implementation of this protocol which shows high practicality. For example, with B=2048, |C|=221, and under 50 Mbps bandwidth and 16 threads, QuickSilver, a state-of-the-art ZK protocol based on vector oblivious linear evaluation (VOLE), can only prove 0.71 million MULT gates per second (mgps) and send one field element per gate; our protocol can prove 15.74 mgps (22x improvement) and send 0.0061 field elements per gate (164x improvement) under the same hardware configuration. (3) Extending the above idea, we constructed a ZK protocol that can prove a single execution of any circuit C in communication O(|C|3/4). This is the first ZK protocol with sublinear communication for an arbitrary circuit in the VOLE-based ZK family.

Title: EchoHand: High Accuracy and Presentation Attack Resistant Hand Authentication on Commodity Mobile Devices Abstract: ABSTRACTBiometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. However, existing hand authentication schemes use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with active acoustic sensing of the other holding hand. EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. Moreover, EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability and security of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks.

Title: Understanding and Mitigating Remote Code Execution Vulnerabilities in Cross-platform Ecosystem Abstract: ABSTRACTJavaScript cross-platform frameworks are becoming increasingly popular. They help developers easily and conveniently build cross-platform applications while just needing only one JavaScript codebase. Recent security reports showed several high-profile cross-platform applications (e.g., Slack, Microsoft Teams, and Github Atom) suffered injection issues, which were often introduced by Cross-site Scripting (XSS) or embedded untrusted remote content like ads. These injections open security holes for remote web attackers, and cause serious security risks, such as allowing injected malicious code to run arbitrary local executables in victim devices (referred to as XRCE attacks). However, until now, XRCE vectors and behaviors and the root cause of XRCE were rarely studied and understood. Although the cross-platform framework developers and community responded quickly by offering multiple security features and suggestions, these mitigations were empirically proposed with unknown effectiveness. In this paper, we conduct the first systematic study of the XRCE vulnerability class in the cross-platform ecosystem. We first build a generic model for different cross-platform applications to reduce their semantic and behavioral gaps. We use this model to (1) study XRCE by comprehensively defining its attack scenarios, surfaces, and behaviors, (2) investigate and study the state-of-the-art defenses, and verify their weakness against XRCE attacks. Our study on 640 real-world cross-platform applications shows, despite the availability of existing defenses, XRCE widely affects the cross-platform ecosystem. 75% of applications may be impacted by XRCE, including Microsoft Teams. (3) Finally, we propose XGuard, a novel defense technology to automatically mitigate all XRCE variants derived from our concluded XRCE behaviors.

Title: CETIS: Retrofitting Intel CET for Generic and Efficient Intra-process Memory Isolation Abstract: ABSTRACTIntel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS. To provide user-friendly interfaces, a CETIS framework was developed, which provides memory file abstraction for the isolated memory regions and a set of APIs to access said regions. CETIS also comes with a compiler-assisted tool chain for users to build secure applications easily. The practicality of using CETIS to protect CPI, CFIXX, and JIT-compilers was demonstrated, and the evaluation reveals that CETIS is performed better than state-of-the-art intra-memory isolation mechanisms, such as MPK.

Title: WINK: Wireless Inference of Numerical Keystrokes via Zero-Training Spatiotemporal Analysis Abstract: ABSTRACTSensitive numbers play an unparalleled role in identification and authentication. Recent research has revealed plenty of side-channel attacks to infer keystrokes, which require either a training phase or a dictionary to build the relationship between an observed signal disturbance and a keystroke. However, training-based methods are unpractical as the training data about the victim are hard to obtain, while dictionary-based methods cannot infer numbers, which are not combined according to linguistic rules like letters are. We observe that typing a number creates not only a number of observed disturbances in space (each corresponding to a digit), but also a sequence of periods between each disturbance. Based upon existing work that utilizes inter-keystroke timing to infer keystrokes, we build a novel technique called WINK that combines the spatial and time domain information into a spatiotemporal feature of keystroke-disturbed wireless signals. With this spatiotemporal feature, WINK can infer typed numbers without the aid of any training. Experimental results on top of software-defined radio platforms show that WINK can vastly reduce the guesses required for breaking certain 6-digit PINs from 1 million to as low as 16, and can infer over 52% of user-chosen 6-digit PINs with less than 100 attempts.

Title: Detecting and Measuring Misconfigured Manifests in Android Apps Abstract: ABSTRACTThe manifest file of an Android app is crucial for app security as it declares sensitive app configurations, such as access permissions required to access app components. Surprisingly, we noticed a number of widely-used apps (some with over 500 million downloads) containing misconfigurations in their manifest files that can result in severe security issues. This paper presents ManiScope, a tool to automatically detect misconfigurations of manifest files when given an Android APK. The key idea is to build a manifest XML Schema by extracting ManiScope constraints from the manifest documentation with novel domain-aware NLP techniques and rules, and validate manifest files against the schema to detect misconfigurations. We have implemented ManiScope, with which we have identified 609,428 (33.20%) misconfigured Android apps out of 1,853,862 apps from Google Play, and 246,658 (35.64%) misconfigured ones out of 692,106 pre-installed apps from 4,580 Samsung firmwares, respectively. Among them, 84,117 (13.80%) of misconfigured Google Play apps and 56,611 (22.95%) of misconfigured pre-installed apps have various security implications including app defrauding, message spoofing, secret data leakage, and component hijacking.

Title: Cross Miniapp Request Forgery: Root Causes, Attacks, and Vulnerability Detection Abstract: ABSTRACTA miniapp is a full-fledged app that is executed inside a mobile super app such as WeChat or SnapChat. Being mini by nature, it often has to communicate with other miniapps to accomplish complicated tasks. However, unlike a web app that uses network domains (i.e., IP addresses) to navigate between different web apps, a miniapp uses a unique global appId assigned by the super app to navigate between miniapps. Unfortunately, any missing checks of the sender's appId in a receiver miniapp can lead to a new type of attacks we name it cross-miniapp request forgery (CMRF). In addition to demystifying the root cause of this attack (i.e., the essence of the vulnerability), this paper also seeks to measure the popularity of this vulnerability among miniapps by developing CmrfScanner, which is able to statically detect the CMRF-vulnerability based on the abstract syntax tree of miniapp code to determine whether there are any missing checks of the appIds. We have tested CmrfScanner with 2,571,490 WeChat miniapps and 148,512 Baidu miniapps, and identified 52,394 (2.04%) WeChat miniapps and 494 (0.33%) Baidu miniapps that involve cross-communication. Among them, CmrfScanner further identified that 50,281 (95.97%) of WeChat miniapps, and 493 (99.80%) of Baidu miniapps lack the appID checks of the sender's mini-apps, indicating that a large amount of miniapp developers are not aware of this attack. We also estimated the impact of this vulnerability and found 55.05% of the lack of validation WeChat miniapps (7.09% of such Baidu miniapps) can have direct security consequences such as privileged data access, information leakage, promotion abuse, and even shopping for free. We hope that our findings can raise awareness among miniapp developers, and future miniapps will not be subject to CMRF attacks.

Title: HeatDeCam: Detecting Hidden Spy Cameras via Thermal Emissions Abstract: ABSTRACTUnlawful video surveillance of unsuspecting individuals using spy cameras has become an increasing concern. To mitigate these threats, there are both commercial products and research prototypes designed to detect hidden spy cameras in household and office environments. However, existing work often relies heavily on user expertise and only applies to wireless cameras. To bridge this gap, we propose HeatDeCam, a thermal-imagery-based spy camera detector, capable of detecting hidden spy cameras with or without built-in wireless connectivity. To reduce the reliance on user expertise, HeatDeCam leverages a compact neural network deployed on a smartphone to recognize unique heat dissipation patterns of spy cameras. To evaluate the proposed system, we have collected and open-sourced a dataset of a total of 22506 thermal and visual images. These images consist of 11 spy cameras collected from 6 rooms across different environmental conditions. Using this dataset, we found HeatDeCam can achieve over 95% accuracy in detecting hidden cameras. We have also conducted a usability evaluation involving a total of 416 participants using both an online survey and an in-person usability test to validate HeatDeCam.

Title: Caulk: Lookup Arguments in Sublinear Time Abstract: ABSTRACTWe present position-hiding linkability for vector commitment schemes: one can prove in zero knowledge that one or m values that comprise commitment \cm all belong to the vector of size N committed to in \com. Our construction \textsfCaulk can be used for membership proofs and lookup arguments and outperforms all existing alternatives in prover time by orders of magnitude. For both single- and multi-membership proofs the \textsfCaulk protocol beats SNARKed Merkle proofs by the factor of 100 even if the latter is instantiated with Poseidon hash. Asymptotically our prover needs O(m^2 + młog N) time to prove a batch of m openings, whereas proof size is O(1) and verifier time is O(łog(łog N)). As a lookup argument, \textsfCaulk is the first scheme with prover time sublinear in the table size, assuming O(Nłog N) preprocessing time and O(N) storage. It can be used as a subprimitive in verifiable computation schemes in order to drastically decrease the lookup overhead. Our scheme comes with a reference implementation and benchmarks.

Title: PalanTír: Optimizing Attack Provenance with Hardware-enhanced System Observability Abstract: ABSTRACTSystem auditing is the foundation of attack provenance to investigate root causes and ramifications of cyber-attacks. However, provenance tracking on coarse-grained audit logs suffers from false causalities caused by dependency explosion. Recent approaches address this problem by increasing provenance granularity using execution partitioning or record-and-replay techniques. Unfortunately, they require program instrumentation and/or impose an unaffordable overhead, which is not practical in deployment. In this paper, we present PalanTír, a provenance-based system that enhances system observability to enable precise and scalable attack investigation. Leveraging hardware-assisted processor tracing (PT), PalanTír optimizes attack provenance in system-call-level audit logs by recovering instruction-level causalities via taint analysis based on PT traces. To reduce the scope of taint analysis and simplify the complexity of taint propagation, PalanTír statically profiles program binaries to identify instructions causally relevant to audit logs and pre-summarize their taint propagation logic at the coarse granularity of basic blocks. Our evaluation against real-life cyber-attacks shows PalanTír's efficiency and effectiveness in attack scenario reconstruction. We also demonstrate that PalanTír can scale to large applications (e.g., Nginx and Sendmail) compiled from upwards of 463,510 lines of C/C++ code.

Title: When Good Becomes Evil: Tracking Bluetooth Low Energy Devices via Allowlist-based Side Channel and Its Countermeasure Abstract: ABSTRACTBluetooth Low Energy (BLE) is ubiquitous today. To prevent a BLE device (e.g., a smartphone) from being connected by unknown devices, it uses allowlisting to allow the connectivity from only recognized devices. Unfortunately, we show that this allowlist feature actually introduces a side channel for device tracking, since a device with the allowed list behaves differently even though it has used randomized MAC addresses. Worse even we also find that the current MAC address randomization scheme specified in Bluetooth protocol is flawed, suffering from a replay attack with which an attacker can replay a sniffed MAC address to probe whether a targeted device will respond or not based on its allowlist. We have validated our allowlist-based side channel attacks with 43 BLE peripheral devices, 11 centrals, and 4 development boards, and found none of them once configured with allowlisting is immune to the proposed attacks. We advocate the use of an interval unpredictable, central and peripheral synchronized random MAC address randomization scheme to defeat passive device tracking (introducing 1% power consumption overhead for centrals and 6.75% for peripherals, and 88.49 μs performance overhead for centrals and 94.46 μs for peripherals), and the use of timestamps to derive randomized MAC addresses such that attackers can no longer be able to replay them to defeat active device tracking (introducing 3.04% overhead for peripherals, and 63.58 μs and 20.54 μs performance overhead for centrals and peripherals). We have disclosed our findings to Bluetooth SIG and many other stake-holders in October 2020. Bluetooth SIG assigned CVE-2020-35473 to track this logical-level protocol flaw. Google assigned our findings as a high severity design flaw and awarded us with a bug bounty.

Title: VOProof: Efficient zkSNARKs from Vector Oracle Compilers Abstract: ABSTRACTThe design of zkSNARKs is increasingly complicated and requires familiarity with a broad class of cryptographic and algebraic tools. This complexity in zkSNARK design also increases the difficulty in zkSNARK implementation, analysis, and optimization. To address this complexity, we develop a new workflow for designing and implementing zkSNARKs, called VOProof. In VOProof, the designer only needs to construct a Vector Oracle (VO) protocol that is intuitive and straightforward to design, and then feeds this protocol to our VO compiler to transform it into a fully functional zkSNARK. This new workflow conceals most algebraic and cryptographic operations inside the compiler, so that the designer is no longer required to understand these cumbersome and error prone procedures. Moreover, our compiler can be fine-tuned to compile one VO protocol into multiple zkSNARKs with different tradeoffs. We apply VOProof to construct three general-purpose zkSNARKs targeting three popular representations of arithmetic circuits: the Rank-1 Constraint System (R1CS), the Hadamard Product Relation (HPR), and the PLONK circuit. These zkSNARKs have shorter and more intuitive descriptions, thus are easier to implement and optimize compared to prior works. To evaluate their performance, we implement a Python framework for describing VO protocols and compiling them into working Rust code of zkSNARKs. Our evaluation shows that the VOProof-based zkSNARKs have competitive performance, especially in proof size and verification time, e.g., both reduced by roughly 50% compared to Marlin (Chiesa et al., EUROCRYPT 2020). These improvements make the VOProof-based zkSNARKs more preferable in blockchain scenarios where the proof size and verification time are critical.

Title: Characterizing and Detecting Non-Consensual Photo Sharing on Social Networks Abstract: ABSTRACTPhoto capturing and sharing have become routine daily activities for social platform users. Alongside the entertainment of social interaction, we are experiencing tremendous visual violation and photo abusing. Especially, users may be unconsciously filmed and exposed online, which is termed as the non-consensual sharing issue. Unfortunately, this problem cannot be well handled with proactive access control or dedicated bystander detection, as users are unaware of their situations and may be filmed stealthily. We propose Videre on behalf of the privacy of the unaware parties in a way that they would be automatically identified and warned before such photos go public. For this, we first elaborate on the predominant features encountered in non-consensual captured photos via a thorough user study. Then we establish a dataset for this context and build a classifier as a proactive detector based on multi-deep-feature fusion. To relieve the burden of person-wise unawareness detection, we further design a signature-based filter for local pre-authorization, which can also implicitly avoid classification errors. We implement and test Videre in various field settings to demonstrate its effectiveness and performance.

Title: Uncovering Intent based Leak of Sensitive Data in Android Framework Abstract: ABSTRACTTo prevent unauthorized apps from retrieving the sensitive data, Android framework enforces a permission based access control. However, it has long been known that, to bypass the access control, unauthorized apps can intercept the Intent objects which are sent by authorized apps and carry the retrieved sensitive data. We find that there is a new (previously unknown) attack surface in Android framework that can be exploited by unauthorized apps to violate the access control. Specifically, we discover that part of Intent objects that are sent by Android framework and carry sensitive data can be received by unauthorized apps, resulting in the leak of sensitive data. In this paper, we conduct the first systematic investigation on the new attack surface namely the Intent based leak of sensitive data in Android framework. To automatically uncover such kind of vulnerability in Android framework, we design and develop a new tool named LeakDetector, which finds the Intent objects sent by Android framework that can be received by unauthorized apps and carry the sensitive data. Applying LeakDetector to 10 commercial Android systems, we find that it can effectively uncover the Intent based leak of sensitive data in Android framework. Specifically, we discover 36 exploitable cases of such kind of data leak, which can be abused by unauthorized apps to steal the sensitive data, violating the access control. At the time of writing, 16 of them have been confirmed by Google, Samsung, and Xiaomi, and we received bug bounty rewards from these mobile vendors.

Title: Non-Distinguishable Inconsistencies as a Deterministic Oracle for Detecting Security Bugs Abstract: ABSTRACTSecurity bugs like memory errors are constantly introduced to software programs, and recent years have witnessed an increasing number of reported security bugs. Traditional detection approaches are mainly specification-based---detecting violations against a specified rule as security bugs. This often does not work well in practice because specifications are difficult to specify and generalize, leaving complicated and new types of bugs undetected. Recent research thus leans toward deviation-based detection which finds a substantial number of similar cases and detects deviating cases as potential bugs. This, however, suffers from two other problems. First, it requires enough similar cases to find deviations and thus cannot work for custom code that does not have similar cases. Second, code-similarity analysis is probabilistic and challenging, so the detection can be unreliable. Sometimes, similar cases can normally have deviating behaviors under different contexts. In this paper, we propose a novel approach for detecting security bugs based on a new concept called Non-Distinguishable Inconsistencies (NDI). The insight is that if two code paths in a function exhibit inconsistent security states (such as being freed or initialized) that are non-distinguishable from the external, such as the callers, there is no way to recover from the inconsistency from the external, which results in a bug. Such an approach has several strengths. First, it is specification-free and thus can support complicated and new types of bugs. Second, it does not require similar cases and by its nature is deterministic. Third, the analysis is practical by minimizing complicated and lengthy data-flow analysis. We implemented NDI and applied it to well-tested programs, including the OpenSSL library, the FreeBSD kernel, the Apache httpd server, and the PHP interpreter. The results show that NDI works for both large and small programs, and it effectively found 51 new bugs, most of which are otherwise missed by the state-of-the-art detection tools.

Title: What Your Firmware Tells You Is Not How You Should Emulate It: A Specification-Guided Approach for Firmware Emulation Abstract: ABSTRACTEmulating firmware of microcontrollers is challenging due to the lack of peripheral models. Existing work finds out how to respond to peripheral read operations by analyzing the target firmware. This is problematic because the firmware sometimes does not contain enough clues to support the emulation or even contains misleading information (e.g., a buggy firmware). In this work, we propose a new approach that builds peripheral models from the peripheral specification. Using NLP, we translate peripheral behaviors in human language (documented in chip manuals) into a set of structured condition-action rules. By checking, executing, and chaining them at run time, we can dynamically synthesize a peripheral model for each firmware execution. The extracted condition-action rules might not be complete or even be wrong. We, therefore, propose incorporating symbolic execution to quickly pinpoint the root cause. This assists us in the manual correction of the problematic rules. We have implemented our idea for five popular MCU boards spanning three different chip vendors. Using a new edit-distance-based algorithm to calculate trace differences, our evaluation against a large firmware corpus confirmed that our prototype achieves much higher fidelity compared with state-of-the-art solutions. Benefiting from the accurate emulation, our emulator effectively avoids false positives observed in existing fuzzing work. We also designed a new dynamic analysis method to perform driver code compliance checks against the specification. We found some non-compliance which we later confirmed to be bugs caused by race conditions.

Title: Perils and Mitigation of Security Risks of Cooperation in Mobile-as-a-Gateway IoT Abstract: ABSTRACTMobile-as-a-Gateway (MaaG) is a popular feature using mobile devices as gateways to connect IoT devices to cloud services for management. MaaG IoT access control systems support remote access sharing/revocation while allowing "offline availability'' for better usability. Realizing these functionalities requires secure cooperation among the cloud service, the companion app, and the IoT device. For practical considerations, we find that almost all cloud services perform access model translation (AMT) to translate expressive cloud-side access policies to simple device-side policies. During the process, ad-hoc protocols are developed to support the access policy synchronization. Unfortunately, current MaaG IoT systems fail to recognize the security risks in the process of access model translation and synchronization. We analyze ten top-of-the-line MaaG IoT devices and find that all of them have serious vulnerabilities, e.g., allowing irrevocable and permanent access for temporary users. We further propose a secure protocol design that defends against all identified attacks.

Title: Poster: Backdoor Attacks on Spiking NNs and Neuromorphic Datasets Abstract: ABSTRACTNeural networks provide state-of-the-art results in many domains. Yet, they often require high energy and time-consuming training processes. Therefore, the research community is exploring alternative, energy-efficient approaches likespiking neural networks (SNNs). SNNs mimic brain neurons by encoding data into sparse spikes, resulting in energy-efficient computing. To exploit the properties of the SNNs, they can be trained with neuromorphic datasets that capture the differences in motion. SNNs, just like any neural network model, can be susceptible to security threats that make the model perform anomalously. One of the most crucial threats is the backdoor attacks that modify the training set to inject a trigger in some samples. After training, the neural network will perform correctly on the main task. However, under the presence of the trigger (backdoor) on an input sample, the attacker can control its behavior. The existing works on backdoor attacks consider standard datasets and not neuromorphic ones. In this paper, to the best of our knowledge, we present the first backdoor attacks on neuromorphic datasets. Due to the structure of neuromorphic datasets, we utilize two different triggers, i.e., static andmoving triggers. We then evaluate the performance of our backdoor using spiking neural networks, achieving top accuracy on both main and backdoor tasks, up to 99%.

Title: Poster: Correctness of n-parties ECDSA By the Claim of Byzantine Agreement Abstract: ABSTRACTECDSA is widely used in transport layer security, cryptocurrency, and more. The problem of securely computing ECDSA has received considerable interest. Literature suggests that there is no n-parties ECDSA which shows the property of correctness (where correctness means when any party forgets to send the message to another party, and still the system provides the correct output). In this poster, we proposed a novel approach for achieving n-parties ECDSA with correctness using the claim of Byzantine agreement. We present our work by using the existing work of Xue et al. [1] for signature generation. Later on, we applied the Byzantine agreement. Our work easily deals with the case when any party does not send the message to another party.

Title: Poster: Ontology Enabled Chatbot for Applying Privacy by Design in IoT Systems Abstract: ABSTRACTOur aim is to create a personal assistant, a chatbot, that can answer queries from software developers regarding Privacy by Design (PbD) methods and applications throughout the design phase of IoT system development. We used semantic web technologies to model the PARROT Ontology that includes knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system needs, and the privacy patterns that should be applied across IoT systems. To determine the PARROT ontology's requirements, a collection of real-world IoT use cases were aided by a series of workshops to gather Competency Questions (CQs) from researchers and software engineers, resulting in 81 selected CQs. In a user study, the PARROT ontology was able to answer up to 58% of software developers' privacy-related issues. The technical report \citeorca149337 contains further analysis and results from data collecting and intermediate synthesis steps.

Title: Poster: A Post-Quantum Oblivious PRF from Isogenies Abstract: ABSTRACTAt Asiacrypt 2020, Boneh et al. proposed a verifiable oblivious pseudorandom function based on isogenies. Basso et al. later demonstrated a subexponential attack on the pseudorandomness of the protocol. In this work, we propose an efficient countermeasure against such an attack. We also propose several improvements that significantly reduce the computational and communication cost of the protocol. We introduce some countermeasures that make the protocol secure against the recent SIDH attacks. Putting everything together, we obtain the most efficient post-quantum OPRF protocol.

Title: Poster: Efficient Three-Party Shuffling Using Precomputation Abstract: ABSTRACTIn this paper, we revisit the problem of secure shuffling in a three-server setting with an honest majority. We begin with the recent work of Araki. et al. (CCS'21) and use precomputation to improve the communication and round complexity of the online phase of their shuffle protocol. Our simple yet effective shuffling method is not limited to three parties and can be used in a variety of situations. Furthermore, the design of our solution allows for fine tuning to achieve improved efficiency based on the underlying application's parameters. Our protocols are initially presented with semi-honest security and then extended to support malicious corruption.

Title: Poster: Data Recovery from Ransomware Attacks via File System Forensics and Flash Translation Layer Data Extraction Abstract: ABSTRACTRansomware is increasingly prevalent in recent years. To defend against ransomware in computing devices using flash memory as external storage, existing designs extract the entire raw flash memory data to restore the external storage to a good state. However, they cannot allow a fine-grained recovery in terms of user files as raw flash memory data do not have the semantics of "files''. In this work, we design FFRecovery, a new ransomware defense strategy that can support fine-grained data recovery after the attacks. Our key idea is, to recover a file corrupted by the ransomware, we can 1) restore its file system metadata via file system forensics, and 2) extract its file data via raw data extraction from the flash translation layer, and 3) assemble the corresponding file system metadata and the file data. A simple prototype of FFRecovery has been developed and some preliminary results are provided.

Title: Poster: Inaudible Acoustic Noise from Silicon Capacitors for Voice-Command Injection Abstract: ABSTRACTMulti-layer ceramic capacitors (MLCCs), commonly used in electronics products, can generate acoustic noise driven by electrical deviation. Such acoustic noise has been exploited to attack systems' security. In particular, CapSpeaker silently delivers voice commands to voice-controllable systems using inaudible acoustic noise and intermodulation distortion (IMD). Silicon capacitor is an emerging technology that achieves a larger capacitance with a smaller footprint fabricated with semiconductor manufacturing processes using different structures and materials. Do silicon capacitors still generate acoustic noise despite the differences? We positively answer the question by experimentally showing that an off-the-shelf silicon capacitor generates audible acoustic noise through IMD; CapSpeaker is still feasible with the new capacitor

Title: Poster: An Analysis of Privacy Features in 'Expert-Approved' Kids' Apps Abstract: ABSTRACTDuring the course of the past decade, children have become avid consumers of digital media through mobile devices. The industry for children's mobile applications is booming and marketplaces offer categories of apps aimed specifically at children. In this study, we perform a mixed-methods privacy analysis of 137 'expert-approved' children's apps from the Google Play Store. Our findings show that these apps do not sufficiently support children to exercise their privacy rights, whilst simultaneously making use of libraries and data trackers which may collect and share sensitive user data.

Title: Poster: A Systems Approach to GDPR Compliance-by-Design in Web Development Stacks Abstract: ABSTRACTPressured by existing regulations such as the EU GDPR, online services must advertise a personal data protection policy declaring the types and purposes of collected personal data, which must then be strictly enforced as per the consent decisions made by the users. However, due to the lack of system-level support, obtaining strong guarantees of policy enforcement is hard, leaving the door open for software bugs and vulnerabilities to cause GDPR-compliance violations. We present ongoing work on building a GDPR-aware personal data policy compliance system for web development frameworks. Currently prototyped for the MERN framework, our system allows web developers to specify a GDPR manifest from which the data protection policy of the web application is automatically generated and is transparently enforced through static code analysis and runtime access control mechanisms. GDPR compliance is checked in a cross-cutting manner requiring few changes to the application code. We evaluate our prototype with four real-world applications. Our system can model realistic GDPR data protection requirements, adds modest performance overheads to the web application, and can detect GDPR violation bugs.

Title: Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs Abstract: ABSTRACTOver the last two years, governments all over the world have used a variety of containment measures to control the spread of \covid, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented in actuality. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, concretely detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework that enables the execution of a wide range of standard epidemiological models for any infectious disease on a population's most recent real contact graph while keeping all contact information private locally on the participants' devices. Our theoretical constructs are supported by a proof-of-concept implementation in which we show that a 2-week simulation over a population of half a million can be finished in 7 minutes with each participant consuming less than 50 KB of data.

Title: Poster EveGAN: Using Generative Deep Learning for Cryptanalysis Abstract: ABSTRACTCryptography and Machine Learning are two computational science fields that intuitively seem related. Privacy-preserving machine learning-either utilizing encrypted models or learning over encrypted data-is an exploding field thanks to the maturation of primitives such as fully homomorphic encryption and secure multiparty computation. However there has been surprisingly little work on applying recent advances in machine learning to the task of cryptanalysis, the branch of cryptography that studies how cryptographic ciphers can be attacked. In particular, while a cryptographic cipher seeks to keep certain information secret by making it appear random, discerning patterns and structure from random data is a common machine learning task. This paper proposes EveGAN, an approach that treats cryptanalysis as a language translation problem. While treating cipher cracking as a language translation problem has been validated against a handful of classical substitution ciphers, the EveGAN approach builds on these results to create a new class of generative deep learning-based cryptanalysis attacks.

Title: Poster: A Novel Formal Threat Analyzer for Activity Monitoring-based Smart Home Heating, Ventilation, and Cooling Control System Abstract: ABSTRACTContemporary home control systems determine real-time heating/cooling demands utilizing smart sensor devices, giving rise to demand control heating, ventilation, and cooling (DCHVAC) systems, thus improving the home's energy efficiency. The adoption of activity monitoring in the smart home control system further augments the controller efficiency and improves occupants' comfort and productivity, elderly monitoring, and so forth. Additionally, the learned occupants' activity patterns help embed machine learning (ML)-based abnormality detection capability to track inconsistencies among the zone sensor measurements. Hence, the incorporation of an activity monitoring system assists anomaly detection models (ADMs) in detecting false data injection (FDI) attacks that are being glowingly researched due to their massive damage capability. However, in this work, we propose a novel attack strategy that identified that the knowledge of occupants' activities along with indoor air quality (IAQ) and occupancy sensor measurements allows the attackers to launch even more hazardous attack (i.e., significant increment in energy cost/ worsening health conditions for the occupants). Hence, it is crucial to analyze the security of the activity monitoring-based smart home DCHVAC system. Accordingly, we propose a novel formal threat analyzer that analyzes the threat space of the smart home DCHVAC control system, which is modeled by rule-based control policies and ML-based ADMs. The rules from the ADM are extracted through an efficient algorithm. The constraints associated with the rules are solved through a satisfiability module theorem (SMT)-based solver. %We performed our initial evaluation of the proposed threat analyzer's effectiveness on the Center of Advanced Studies in Adaptive Systems (CASAS) dataset using some metrics. We will further experiment with other metrics along experimenting with our collaborator's dataset (KTH live-in lab) and open-source Örebro datasets for assessing the framework with realistic occupants' activity. Moreover, we also created our prototype testbed for evaluating the feasibility of the proposed attack and threat analyzer.

Title: Poster: The Unintended Consequences of Algorithm Agility in DNSSEC Abstract: ABSTRACTCryptographic algorithm agility is an important property for DNSSEC: it allows easy deployment of new algorithms if the existing ones are no longer secure. In this work we show that the cryptographic agility in DNSSEC, although critical for provisioning DNS with strong cryptography, also introduces a vulnerability. We find that under certain conditions, when new algorithms are listed in signed DNS responses, the resolvers do not validate DNSSEC. As a result, domains that deploy new ciphers may in fact cause the resolvers not to validate DNSSEC. We exploit this to develop DNSSEC-downgrade attacks and experimentally and ethically evaluate them against popular DNS resolver implementations, public DNS providers, and DNS services used by web clients worldwide. We find that major DNS providers as well as 45% of DNS resolvers used by web clients are vulnerable to our attacks.

Title: Poster: MSILDiffer - A Security Patch Analysis Framework Based on Microsoft Intermediate Language for Large Software Abstract: ABSTRACTIn this poster, we proposed a .NET patch analysis framework named MSILDiffer based on Microsoft Intermediate Language (MSIL). First, MSILDiffer directly extracts MSIL instructions from the .NET assemblies, and retrieves the hierarchy of classes as well as their internal class methods. Then, with coarse and fine granularity feature extraction and comparison, MSILDiffer quickly filters out the code with substantial changes after patch. Besides, we build a dataset of patch analysis containing 24.46 million class methods based on the Microsoft Exchange mail system security patches. With the assistance of MSILDiffer, we generated 32 call paths and crafted corresponding POCs for 1-day vulnerabilities in the dataset. Through the experiment evaluation, MSILDiffer is superior to JustAssembly in terms of coverage, accuracy and time consumption of patch difference analysis.

Title: Poster: Vogue: Faster Computation of Private Heavy Hitters Abstract: ABSTRACTConsider a set of N clients, each of which holds a private input string. An input string that is held by at least τ clients is defined as a τ-heavy hitter. In various application scenarios, data-aggregation servers are interested in learning τ-heavy hitters. To ensure that the servers do not learn anything about client input in the process, the problem of identifying heavy hitters privately is gaining popularity. Towards this, we design a novel system called Vogue, which provides improved efficiency as well as security guarantees over the state-of-the-art system of Poplar. Concretely, Vogue provides up to 27x efficiency improvement over Poplar when considering 400,000 clients who hold 256-bit input strings. Moreover, Vogue overcomes intermediate information leakages present in Poplar and guarantees full security in the presence of a malicious adversary. In the process of designing Vogue, we also design secure and efficient protocols for stable compaction and shuffle, each of which improves over its respective state-of-the-art.

Title: Poster: Coded Broadcast for Scalable Leader-Based BFT Consensus Abstract: ABSTRACTWith the success of blockchains and cryptocurrencies, Byzantine Fault Tolerant state machine replication protocols have attracted considerable interest. A class of such protocols that is particularly popular are leader-based protocols, where one server (the leader) is tasked with proposing and broadcasting blocks of new data to be applied to the state machine. Simple implementation of the broadcast requires the leader to send entire blocks to all other servers, creating a network bottleneck at the leader and reducing the system throughput as the number of servers scales. We demonstrate this effect by benchmarking HotStuff, a popular leader-based protocol, and then propose a mitigation based on coding. The key idea is to let the leader encode the block into small chunks, and task each server with broadcasting a chunk, thus utilizing the bandwidth of all servers during the process. We apply this idea on HotStuff, and demonstrate a 64% improvement in throughput in a deployment across 9 servers.

Title: Poster MPClan:: Protocol Suite for Privacy-Conscious Computations Abstract: ABSTRACTThe growing volumes of data collected and its analysis to provide better services create worries about digital privacy. The literature has relied on secure multiparty computation techniques to address privacy concerns and give practical solutions. However, recent research has mostly focused on the small-party honest-majority setting of up to four parties, noting efficiency concerns. In this work, we extend the strategies to support a larger number of participants in honest-majority setting with efficiency at the center stage. Designed in the preprocessing paradigm, our semi-honest protocol improves the online complexity of the decade-old state-of-the-art protocol of Damgård and Nielson (CRYPTO'07). In addition to having an improved online communication cost, we can shut down almost half of the parties in the online phase, thereby saving up to 50% in the system's operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for a one-time verification, towards the end. We benchmark popular applications such as deep neural networks, graph neural networks and genome sequence matching using prototype implementations to showcase the practicality of the designed protocols. Our improved protocols aid in bringing up to 60-80% savings in monetary cost over prior work.

Title: Poster: Patient Community -- A Test Bed for Privacy Threat Analysis Abstract: ABSTRACTResearch and development of privacy analysis tools currently suffers from a lack of test beds for evaluation and comparison of such tools. In this work, we propose a benchmark application that implements an extensive list of privacy weaknesses based on the LINDDUN methodology. It represents a social network for patients whose architecture has first been described in an example analysis conducted by one of the LINDDUN authors. We have implemented this architecture and extended it with more privacy threats to build a test bed that enables comprehensive and independent testing of analysis tools.

Title: Poster: EOSDFA: Data Flow Analysis of EOSIO Smart Contracts Abstract: ABSTRACTAs an efficient blockchain platform, EOSIO is becoming increasingly popular. However, it has exposed many security problems and caused a large amount of financial losses. In the past, the difficulty of collecting open-source EOSIO smart contracts and analyzing WebAssembly (Wasm) bytecode compiled by EOSIO smart contracts, making few researchers proposed static analysis tools for EOSIO smart contracts, and tools capable of dataflow analysis have not yet appeared. In this work, we first propose a dataflow analysis method for EOSIO smart contracts. Based on Octopus, we designed an efficient dataflow analysis method, which can generate Static Single Assignment (SSA) form intermediate representation (IR) for the objective function and its variables to obtain the results of dataflow. We further proved the effectiveness of the proposed method through experiments on our collected data sets.

Title: Poster: Cryptographic Inferences for Video Deep Neural Networks Abstract: ABSTRACTDeep neural network (DNN) services have been widely deployed in many different domains. For instance, a client may send its private input data (e.g., images, texts and videos) to the cloud for accurate inferences with pre-trained DNN models. However, significant privacy concerns would emerge in such applications due to the potential data or model sharing. Secure inferences with cryptographic techniques have been proposed to address such issues, and the system can perform secure two-party inferences between each client and cloud. However, most of existing cryptographic systems only focus on DNNs for extracting 2D features for image inferences, which have major limitations on latency and scalability for extracting spatio-temporal (3D) features from videos for accurate inferences. To address such critical deficiencies, we design and implement the first cryptographic inference system, Crypto3D, which privately infers videos on 3D features with rigorous privacy guarantees. We evaluate Crypto3D and benchmark with the state-of-the-art systems on privately inferring videos in the UCF-101 and HMDB-51 datasets with C3D and I3D models. Our results demonstrate that Crypto3D significantly outperforms existing systems (substantially extended to inferences with 3D features): execution time: 186.89x vs. CryptoDL (3D), 63.75x vs. HEANN (3D), 61.52x vs. MP-SPDZ (3D), 45x vs. E2DM (3D), 3.74x vs. Intel SGX (3D), and 3x vs. Gazelle (3D); accuracy: 82.3% vs. below 70% for all of them.

Title: Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? Abstract: ABSTRACTTor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.

Title: Poster: Investigating QUIC's Potential Impact on Censorship Circumvention Abstract: ABSTRACTAlthough not yet ubiquitous, censors and censorship-resistors have been eyeing QUIC as the next avenue for censorship-circumvention. In this poster, we highlight three QUIC features that are avenues for potential improvements to both Pluggable Transports and decoy routing: streams, connections identifiers, and congestion control. We also examine how traffic splitting can be used. We then discuss how these features can be integrated into Pluggable Transports and decoy routing. We close with providing guidance for future work on integrating QUIC into Pluggable Transports and decoy routing.

Title: Poster: Towards Complete Computation Graph Generation for Security Assessment of ROS Applications Abstract: ABSTRACTRobot Operating System (ROS) is a popular middleware suite providing a set of libraries and tools to help with building robot applications. The ROS community makes it possible for developers to compose their own robot applications by simply integrating open-sourced software of different functionalities as standalone processes (ROS nodes) in their own application. These processes communicate with each other through the infrastructure provided by ROS, forming a graph of nodes called computation graph. However, adopting third-party software introduces the possibility of supply-chain attacks. By interacting with other nodes, the third-party ROS nodes seeming to be the most harmless can violate users' privacy, launch denial of service attacks, and even cause danger to human lives, due to the cyber-physical nature of robot applications. To allow effective security assessment of robot applications, we are the first to propose to explore a hybrid program analysis-based method to extract these interactions, i.e. the computation graph, from source code and identify the potentially malicious nodes within the graph.

Title: Poster: RPKI Kill Switch Abstract: ABSTRACTRelying party implementations are an important component of RPKI: they fetch and validate the signed authorizations mapping prefixes to their owners. Border routers use this information to check which Autonomous Systems (ASes) are authorized to originate given prefixes and to enforce Route Origin Validation (ROV) in order to block bogus BGP announcements, preventing accidental and malicious prefix hijacks. In 2021 the RPKI relying party implementations were patched against attacks by malicious publication points. In such attacks the relying parties are stalled processing malformed RPKI objects. In this work we perform a black-box analysis of the patched relying party implementations and find that out of five popular relying parties, two major implementations (Routinator and OctoRPKI) have vulnerabilities that can be exploited to cause large scale blackouts in the RPKI ecosystem. We show that the vulnerabilities we found apply to 84.9% of the networks supporting RPKI. We analyze the code to understand the factors causing the bugs. We show that these vulnerabilities can be exploited to crash the deployed relying parties, disabling RPKI validation and exposing the networks to prefix hijack attacks.

Title: Poster: Physics-Informed Augmentation for Contextual Anomaly Detection in Smart Grid Abstract: ABSTRACTSmart Grid (SG) networks, as a part of critical national infrastructure, are vulnerable to sophisticated cyber-physical attacks. Specifically, a coordinated false data injection attack aiming to generate fake transient measurements in the SG's Automatic Generation Control (AGC), can cause unwarranted actions and blackouts in the worst scenario. Unlike other works that overlook contextual correlations, this work utilizes contextual prior information and a temporal model to detect cyber-attacks. Specifically, we depart from the traditional deep learning anomaly detection, driven by black-box detection; instead, we envision an approach based on physics-informed hybrid deep learning detection. Our approach utilizes the combination of process control-based variational autoencoder, prior knowledge of physics, and long short-term memory for a false data injection attack detection. To the best of our knowledge, our method is the first contextual-based anomaly detection that incorporates process control-based prior information in the smart grid. The proposed approach is evaluated on the modified high-class PowerWorld simulated dataset based on the IEEE 37-bus model. Our experiments observe the lowest reconstruction error and offer 96.9% accuracy, demonstrating superiority over other baselines.

Title: Poster CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models Abstract: ABSTRACTAs the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to "red team' this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to overcome the need to methodically identify and share AI/ML specific vulnerabilities and threat intelligence.

Title: Poster – Towards Authorship Obfuscation with Language Models Abstract: ABSTRACTAuthorship obfuscation is the process of making changes to text such that identifying attributes (style, common words and phrases, tone) are masked. The goal of obfuscation is to retain the semantics of the text (i.e., the meaning) but rewrite it in such a way that the author cannot be identified. In this work, we investigate the effectiveness of language models for authorship obfuscation. More specifically, we examine the application of document summarization (a task where we learn to generate the summary of a text) as an authorship obfuscation method. Since summaries are shorter versions of text but which retain the significant points made in it, we hypothesize that summaries will be stripped off any stylistic identifying features of the text. Our experiments show that this is indeed the case; we were able to fool authorship classifiers and degrade their performance by as much as 70% However, this also significantly affected the semantics; there was a non-trivial loss of information and the produced text was not an accurate representation of the original.

Title: Poster: Adversarial Defense with Deep Learning Coverage on MagNet's Purification Abstract: ABSTRACTMagNet is a defense method that adopts autoencoders to detect and purify adversarial examples. Although MagNet is robust against grey-box and black-box attacks, it is vulnerable to white-box attacks. Despite this prior knowledge, the fundamental reason for and mitigation of the vulnerability of MagNet have not been discussed. We suggest that the challenge of MagNet is the generalization of the data manifold. To explain this, in this work, we leverage deep learning coverage for the reformer of MagNet. We mutate training images through image transformation algorithms and then train the reformer using mutants with new coverage information. The selected mutants provide an interesting data manifold, that cannot be handled by the random noise of MagNet, to the reformer. In grey-box settings, our defense method classified adversarial examples for various perturbation sizes much more accurately than MagNet even with the same architecture. Based on the preliminary result of this work, we consider future work to identify whether the generalization power of deep learning coverage is effective for stronger adversaries and different architectures.

Title: Poster: A WiFi Vision-based Approach to Person Re-identification Abstract: ABSTRACTIn this work, we propose a WiFi vision-based approach to person re-identification (Re-ID) indoors. Our approach leverages the advances of WiFi to visualize a person and utilizes deep learning to help WiFi devices identify and recognize people. Specifically, we leverage multiple antennas on WiFi devices to estimate the two-dimensional angle of arrival (2D AoA) of the WiFi signal reflections to enable WiFi devices to "see'' a person. We then utilize deep learning techniques to extract a 3D mesh representation of a person and extract the body shape and walking patterns for person Re-ID. Our preliminary study shows that our system achieves high overall ranking accuracies. It also works under non-line-of-sight and different person appearance conditions, where the traditional camera vision-based systems do not work well.

Title: Poster: ReMouse Dataset: Measuring Similarity of Human-Generated Trajectories as an Important Step in Dealing with Session-Replay Bots Abstract: ABSTRACTSession-replay bots are believed to be the latest and most advanced generation of web-bots, that are also difficult challenging to defend against. Combating session-replay bots is particularly problematic in online domains that get repeatedly visited by the same genuine human user(s), and possibly in the same/similar way - such as news, banking or gaming sites. Namely, in such domains, it is difficult to determine whether two look-alike sessions are produced by the same human user or these sessions are just bot-generated session replays. In this paper we introduce and provide to the public a novel real-world mouse dynamics dataset named ReMouse. ReMouse dataset is collected in a guided environment and, unlike other publicly available mouse dynamics dataset, it contains repeat-sessions generated by the same human user(s). As such, ReMouse dataset is first of its kind and is of particular relevance for studies on the development of effective defenses against session-replay bots. Our own statistical analysis of ReMouse dataset shows that not only two different human users are highly unlikely to generate same/similar looking sessions when performing the same/similar online task, but even the (repeat) sessions generated by the same human user are likely to be sufficiently distinguishable from one another.

Title: Poster AutoPatch: Automatic Hotpatching of Real-Time Embedded Devices Abstract: ABSTRACTThe number of real-time embedded devices is increasing, especially in critical places such as industrial and medical devices. These devices are the target of many security attacks; therefore, their security must be ensured, and existing vulnerabilities must be fixed immediately. Typical update approaches require rebooting or halting the devices for an unpredictable time, and are hence not applicable for real-time embedded devices such as medical devices, which must run continuously without rebooting. Hotpatching, which patches the code without rebooting the device, has been used in this context. However, existing hotpatching methods require manual effort from programmers that is error-prone and time-consuming. Further, little attention has been paid to these techniques for real-time embedded devices. This paper proposes AutoPatch, the first automatic hotpatching approach for real-time embedded devices. AutoPatch automatically analyzes the official patch to extract its semantics using predicate abstraction, and generates a semantically equivalent patch called hotpatch. Our initial results show that AutoPatch can automatically generate hotpatches correctly based on the official patches (i.e., real-world CVEs) using program analysis. We also validate that the generated hotpatch can fix the vulnerabilities without rebooting or halting the devices.

Title: Poster: MUSTARD - Adaptive Behavioral Analysis for Ransomware Detection Abstract: ABSTRACTBehavioural analysis based on filesystem operations is one of the most promising approaches for the detection of ransomware. Nonetheless, tracking all the operations on all the files for all the processes can introduce a significant overhead on the monitored system. We present MUSTARD, a solution to dynamically adapt the degree of monitoring for each process based on their behaviour to achieve a reduction of monitoring resources for the benign processes.

Title: Poster: Towards Large-Scale Measurement Study on LiDAR Spoofing Attacks against Object Detection Abstract: ABSTRACTLiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing of the surrounding environment. The recent rapid deployment of autonomous driving (AD) has highly benefited from the advancement of LiDARs. At the same time, the safety-critical application strongly motivates its security research. Recent studies demonstrate that they can manipulate the LiDAR point cloud and fool object detection by shooting malicious lasers against LiDAR scanning. However, prior efforts focus on limited types of LiDARs and object detection models, and their threat models are not clearly validated in the real world. To fill the critical research gap, we plan to conduct the first large-scale measurement study on LiDAR spoofing attacks against a wide variety of LiDARs with major object detectors. To perform this measurement, we first significantly improved the LiDAR spoofing capability (30x more spoofing points than the prior attack) with more careful optics and functional electronics, which allows us to be the first to clearly demonstrate and quantify key attack capabilities assumed in prior works. In this poster, we present our preliminary results on VLP-16 and our research plan.

Title: Poster: INSIDE - Enhancing Network Intrusion Detection in Power Grids with Automated Facility Monitoring Abstract: ABSTRACTAdvances in digitalization and networking of power grids have increased the risks of cyberattacks against such critical infrastructures, where the attacks often originate from within the power grid's network. Adequate detection must hence consider both physical access violations and network anomalies to identify the attack's origin. Therefore, we propose INSIDE, combining network intrusion detection with automated facility monitoring to swiftly detect cyberattacks on power grids based on unauthorized access. Besides providing an initial design for INSIDE, we discuss potential use cases illustrating the benefits of such a comprehensive methodology.

Title: Poster: Insights into Global Deployment of RPKI Validation Abstract: ABSTRACTIP prefix hijacks, due to malicious attacks or benign misconfigurations, pose a threat to the Internet's stability and security. RPKI was designed to enable networks to block prefix hijacks by enforcing Route Origin Validation (ROV). In this work we evaluate the effectiveness of the global ROV deployment in blocking prefix hijacks. We perform control-plane and data-plane experiments and provide an in-depth analysis of the collected results. Our analysis is based on new methodologies we developed that allow more accurate identification of ROV enforcing ASes. Our analysis shows that the current ROV enforcement rate is significantly higher than found in previous studies: in contrast to 0.6% in a study from 2021, in our work we find that 37.8% enforce ROV. Our results indicate that ROV has finally gained traction and offers substantial protection against prefix hijacks.

Title: Poster: DNS in Routers Considered Harmful Abstract: ABSTRACTTo save costs residential routers often do not implement most of the functionalities and security features of DNS, yet they still contain DNS forwarders which merely proxy the clients' requests to another address. These forwarders separate the network configuration of the internal client network from the network of the ISP. This provides connectivity without the need for synchronization. History of cache poisoning attacks shows however that such simplified implementations expose a wide range of vulnerabilities. We propose to remove DNS from routers. We show that the performance impact is negligible, while security gain is substantial. We discuss a number of ways for implementing our approach

Title: Poster: Privacy-preserving Genome Analysis using Verifiable Off-Chain Computation Abstract: ABSTRACTGenome-wide association studies (GWAS) focus on finding associations between genotypes and phenotypes such as susceptibility to diseases. Since genetic data is extremely sensitive and long-lived, individuals and organizations are reluctant to share their data for analysis. This paper proposes two solutions for a fully decentralized and privacy-preserving system for performing minor allele frequency analysis on multiple data sets. Homomorphic encryption and zero-knowledge proofs are used in combination with a blockchain system to achieve data privacy and enable verifiability. Preliminary evaluation of the solutions reveals several important challenges such as handling large cipher texts in smart contracts and reuse of the encrypted data for specific researcher queries that need to be tackled in order to make the solutions more practical.

Title: Poster: On the System-Level Effectiveness of Physical Object-Hiding Adversarial Attack in Autonomous Driving Abstract: ABSTRACTIn Autonomous Driving (AD) systems, perception is both security and safety-critical. Among different attacks on AD perception, object-hiding adversarial attack is one of the most critical ones due to the direct impact on safety-critical driving decisions such as collision avoidance. However, all of the prior works on physical object-hiding adversarial attacks only study the security of the AI component alone rather than with the entire AD system pipeline with closed-loop control. This thus inevitably raises a critical research question: can these prior works actually achieve system-level effects (e.g., vehicle collisions, traffic rule violation) under real-world AD settings with closed-loop control? To answer this critical question, in this work we take the necessary first step by performing the first measurement study on whether and how effective the existing designs can lead to system-level effects. Our early results find that RP2 and FTE, as two representative examples of prior works, cannot achieve any system-level effect in a representative closed-loop AD setup in common STOP sign-controlled road speeds. In the future, we plan to 1) perform a more comprehensive measurement study using both simulated environments and a real vehicle-sized AD R&D chassis; and 2) analyze the measurement study results and explore new attack designs that can better achieve the system-level effect in AD systems.

Title: Poster: Enabling Cost-Effective Blockchain Applications via Workload-Adaptive Transaction Execution Abstract: ABSTRACTAs transaction fees skyrocket today, blockchains become increasingly expensive, hurting their adoption in broader applications. This work tackles the saving of transaction fees for economic blockchain applications. The key insight is that other than the existing "default'' mode to execute application logic fully on-chain, i.e., in smart contracts, and in fine granularity, i.e., user request per transaction, there are alternative execution modes with advantages in cost-effectiveness. On Ethereum, we propose a holistic middleware platform supporting flexible and secure transaction executions, including off-chain states and batching of user requests. Furthermore, we propose control-plane schemes to adapt the execution mode to the current workload for optimal runtime cost. We present a case study on the institutional accounts (e.g., coinbase.com) intensively sending Ether on Ethereum blockchains. By collecting real-life transactions, we construct workload benchmarks and show that our work saves 18%\sim 47%18%-47% per invocation than the default baseline while introducing 1.81%\sim 16.59%1.81%-16.59% blocks delay.

Title: Poster: Fingerprint-Face Friction Based Earable Authentication Abstract: ABSTRACTEar wearables (earables) have become an emerging and wide acceptable platform for various applications. Because of the limited input interface of earables, traditional authentication methods become less desired. However, the feature-rich sensing abilities of earables and the unique human face-ear channel bring us new sensing opportunities to reutilize fingerprints. In this work, we proposed SlidePass, a secure earables authentication system that leverages the finger-face acoustic friction produced by sliding finger gestures on the face. In particular, our system leverages the inward-facing microphone of the earables to reliably capture the acoustic of finger-face frictions. The core insight of our system is to utilize the face as a natural scanner for finger-face friction and earables to capture and reconstruct the fingerprint features. SlidePass is specially designed for earables. Due to the finger-face friction captured and encrypted by the face channel that is unique and hidden in the human skull, SlidePass is more resistant to various spoofing attacks. Our preliminary evaluation included ten different fingerprints showing that SlidePass achieves an average accuracy of 94%.

Title: Poster: Clean-label Backdoor Attack on Graph Neural Networks Abstract: ABSTRACTGraph Neural Networks (GNNs) have achieved impressive results in various graph learning tasks. They have found their way into many applications, such as fraud detection, molecular property prediction, or knowledge graph reasoning. However, GNNs have been recently demonstrated to be vulnerable to backdoor attacks. In this work, we explore a new kind of backdoor attack, i.e., a clean-label backdoor attack, on GNNs. Unlike prior backdoor attacks on GNNs in which the adversary can introduce arbitrary, often clearly mislabeled, inputs to the training set, in a clean-label backdoor attack, the resulting poisoned inputs appear to be consistent with their label and thus are less likely to be filtered as outliers. The initial experimental results illustrate that the adversary can achieve a high attack success rate (up to 98.47%) with a clean-label backdoor attack on GNNs for the graph classification task. We hope our work will raise awareness of this attack and inspire novel defenses against it.

Title: Poster: Unanimous-Majority - Pushing Blockchain Sharding Throughput to its Limit Abstract: ABSTRACTBlockchain sharding protocols randomly distribute nodes to different shards. They limit the quantity of shards to ensure that the adversary remains a minority inside each shard with a high probability. There can exist only a small number of shards. In this article, we propose a new sharding protocol that links the number of shards with the adversary population in real-time instead of a fixed upper-bounded population. The protocol is a two-phase design. First, several committee shards are constructed where the majority of nodes inside each are honest with high probability; then, each committee shard randomly splits into several worker shards with a high likelihood that at least one honest node is inside each. Each worker shard handles different transactions. Worker shard blocks that did not pass the unanimous voting are collected and voted by the committee shard using the majority voting. We show that (1) in the worst case (extremely unlikely) when all the transactions need to be handled by the committee shards, the transaction throughput and the data requirement only deteriorate to the same level as classical sharded blockchain; (2) when the worker shards handle most transactions, the overall transaction throughput is zoomed by two magnitudes securely while the data requirement for nodes remains at the same level.

Title: Poster: User-controlled System-level Encryption for all Applications Abstract: ABSTRACTToday, some applications encrypt our data, while many others do not. Users must accept the level of protection the application provides. Our research aims to support client-to-client encryption at the system level so that users can enable encryption for data in any application (e.g., email, Slack), even if the application does not support it. Two users can exchange sensitive data without it being accessible to any applications or systems along the path. We will describe the challenges to designing the system, the techniques we will use to build the system, and the advantages of encryption at the system level.

Title: Poster: Automated Discovery of Sensor Spoofing Attacks on Robotic Vehicles Abstract: ABSTRACTRobotic vehicles are playing an increasingly important role in our daily life. Unfortunately, attackers have demonstrated various sensor spoofing attacks that interfere with robotic vehicle operations, imposing serious threats. Thus, it is crucial to discover such attacks earlier than attackers so that developers can secure the vehicles. In this paper, we propose a new sensor fuzzing framework SensorFuzz that can systematically discover potential sensor spoofing attacks on robotic vehicles. It generates malicious sensor inputs by formally modeling the existing sensor attacks and leveraging high-fidelity vehicle simulation, and then analyzes the impact of the inputs on the vehicle with a resilience-based feedback mechanism.

Title: Poster: TaintGrep: A Static Analysis Tool for Detecting Vulnerabilities of Android Apps Supporting User-defined Rules Abstract: ABSTRACTIn this poster, we present TaintGrep, a novel static analysis approach to detect vulnerabilities of Android applications. This approach combines the advantages of semantic pattern matching and taint analysis to get better accuracy and be able to detect cross-function vulnerabilities. Compared with many traditional tools, TaintGrep does not require the full source code or building environment to analyze. Moreover, it supports users in defining their customized matching rules using their vulnerability mining experience, which makes this approach more flexible and scalable. In the preliminary experiment, we give a detailed analysis of the rules of two typical vulnerabilities: generic DoS and arbitrary file read/write, and have detected 77 0day vulnerabilities with these rules in 16 well-known Android applications.

Title: Poster: May the Swarm Be With You: Sensor Spoofing Attacks Against Drone Swarms Abstract: ABSTRACTSwarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been paid to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target one swarm member (target drone) through sensor spoofing attacks, and indirectly cause other swarm members (victim drones) to veer off from their course, and potentially resulting in a crash. Our attack cannot be prevented by traditional software security techniques, and it is stealthy in nature as it causes seemingly benign deviations in drone swarms. Our initial results show that spoofing the position of a target drone by 5m is sufficient to cause other drones to crash into a front obstacle. Overall, our attack achieves 76.67% and 93.33% success rate with 5m and 10m spoofing deviation respectively.

Title: Demo: End-to-End Wireless Disruption of CCS EV Charging Abstract: ABSTRACTThe shift from vehicles with internal combustion engines (ICE) to fully Electric Vehicles (EVs) is happening at a rapid pace. To be competitive with ICEs and ensure a smooth rollout, the charging process of EVs needs to be as fast and convenient as possible. Modern DC fast-charging standards achieve this by implementing a high-level charging communication (HLC), which enables a safe, efficient, and convenient charging experience. However, with the introduction of and reliance on HLC, the security of the communication becomes essential. As we have recently shown, the PLC-based charging communication used by the Combined Charging System (CCS) is vulnerable to a denial-of-service attack. The attack, named Brokenwire, exploits the CSMA/CA medium access mechanism and relies on continuously broadcasting a preamble to force the communication participants to back off to prevent interference. In this demonstration, we present an end-to-end approach for conducting the novel Brokenwire attack. We present the architecture of our software pipeline and demonstrate how it allows an adversary to capture and transmit the attack signal with just one click.

Title: Demo -- MaLFraDA: A Machine Learning Framework with Data Airlock Abstract: ABSTRACTTraining machine learning algorithms on sensitive, illegal to possess, and psychologically harmful data is challenging because researchers have to do training without handling the data. Moreover, the nature of the data imposes strict control, monitoring, and examination of all the activities involved, including communication, execution, and release of algorithms, datasets, outputs, and results. In this regard, this work proposes a new multi-zoned framework called MaLFraDA. MaLFraDA has soft air gaps between its zones to isolate and control communication in and out of the framework. Besides, it includes (i) a vetter to investigate and approve incoming model/algorithm, and outgoing information, (ii) encrypted data vaults, and (iii) airlock instances for secure execution/computation. MaLFraDA, with an extension, runs popular distributed machine learning algorithms such as federated and split learning using multiple data custodians.

Title: Demo: VaxPass -- A Scalable and Verifiable Platform for COVID-19 Records Abstract: ABSTRACTCOVID-19 has altered the landscape of medical record issuing and verification. Multiple challenges have arisen in this new era as individuals are now required to prove their health status for traveling, working, or simply eating at a restaurant. Record verification across country borders is particularly hard to achieve as it requires collaboration at an international level, sharing potentially sensitive medical data. In this work, we propose VaxPass, a scalable system for COVID-19 record issuing and verification that facilitates this collaboration with minimal data leakage. At the core of our design lies a 2-tier blockchain architecture that allows individual issuing authorities to maintain their own 1st -level blockchain and only upload a small digest of their records, periodically, on the 2nd -level. Crucially, a verifier can check the validity of a certificate without having access to the 1st -level blockchain where the records actually reside. Our system also includes a mobile application and a web client. As we demonstrate, its performance scales well with the number of participants, making this the first solution able to support real-life inspired needs for such a system, while maintaining confidentiality of the medical data solely to privy entities.

Title: WAHC'22: 10th Workshop on Encrypted Computing and Applied Homomorphic Cryptography Abstract: ABSTRACTThe 10th Workshop on Encrypted Computing and Applied Homomorphic Cryptography is held in Los Angeles, CA, USA on November 7, 2022, co-located with the ACM Conference on Computer and Communications Security (CCS). The workshop aims to bring together professionals, researchers and practitioners from academia, industry and government in the area of computer security and applied cryptography with an interest in practical applications of homomorphic encryption, encrypted computing, functional encryption and secure function evaluation, private information retrieval and searchable encryption. The workshop will feature 6 exciting accepted talks on different aspects of secure computation and a forum to discuss current and future challenges. Additionally, the workshop will feature one keynote presentation, as well as a working session. The complete WAHC'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3560827.

Title: CPSIoTSec '22: 4th Workshop on CPS & IoT Security and Privacy Abstract: ABSTRACTThere is a rapidly growing interest in the security of cyber-physical systems (CPS) and internet-of-things (IoT) in industry, government and academia. This interest is also reflected in this workshop that from 2019 has been part of the ACM Conference on Computer and Communications Security, which originally hosted two workshops: One on CPS security and privacy (running 5 times in the past) and one on IoT security and privacy (running 2 times in the past). Due to the close connection of the two topics, the Steering Committees of the two workshops decided in 2020 to merge towards delivering a world-class event on CPS&IoT security and privacy. CPSIoTSec'22 corresponds to the fourth edition of this merger. CPS&IoTSec'22 attracted 16 submissions, which were assigned to four independent reviewers. Following a rigorous peer-review process, 3 full papers and 4 short papers were accepted, achieving an acceptance rate of 43.75%. We would like to express our deepest thanks to the CPS&IoTSec'22 Steering Committee for entrusting us with organizing the workshop, and all the members of the program committee for their valuable reviews and discussions. Likewise, we would like to thank all the authors who submitted their contributions in the CPS & IoT Security field. The complete CPSIoTSec'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3560826

Title: WPES '22: 21st Workshop on Privacy in the Electronic Society Abstract: ABSTRACTThese proceedings contain the papers selected for inclusion in the technical program for the 21st ACM Workshop on Privacy in the Electronic Society (WPES 2022), held in conjunction with the 29th ACM Conference on Computer and Communication Security (CCS 2022). This year, WPES is held as a hybrid event (including both in-person and online presentations) on November 7, 2022. In response to the workshop's call for papers, 59 valid submissions were received. These 59 submissions include 43 submissions as full papers and 16 submissions as short papers. They were evaluated by a technical program committee consisting of 51 researchers whose backgrounds include a diverse set of topics related to privacy. Each paper was reviewed by at least 3 members of the program committee, and the average number of reviews for each paper is 3.75. Papers were evaluated based on their importance, novelty, and technical quality. After the rigorous review process, 12 submissions were accepted as full papers (acceptance rate: 20.3%) and additionally 8 submissions were accepted as short papers. The complete workshop proceedings are available at the following URL: https://dl.acm.org/doi/proceedings/10.1145/3559613.

Title: CCSW '22: The 2022 Cloud Computing Security Workshop Abstract: ABSTRACTClouds and massive-scale computing infrastructures are starting to dominate computing and will likely continue to do so for the foreseeable future. Major cloud operators are now comprising millions of cores hosting substantial fractions of corporate and government IT infrastructure. CCSW is the world's premier forum bringing together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including: ·Side channel attacks ·Cryptographic protocols for cloud security ·Secure cloud resource virtualization mechanisms ·Secure data management outsourcing (e.g., database as a service) ·Privacy and integrity mechanisms for outsourcing ·Foundations of cloud-centric threat models ·Secure computation outsourcing ·Remote attestation mechanisms in clouds ·Sandboxing and VM-based enforcements ·Trust and policy management in clouds ·Secure identity management mechanisms ·Cloud-aware web service security paradigms and mechanisms ·Cloud-centric regulatory compliance issues and mechanisms ·Business and security risk models and clouds ·Cost and usability models and their interaction with security in clouds ·Scalability of security in global-size clouds ·Binary analysis of software for remote attestation and cloud protection ·Network security (DOS, IDS etc.) mechanisms for cloud contexts ·Security for emerging cloud programming models ·Energy/cost/efficiency of security in clouds ·mOpen hardware for cloud ·Machine learning for cloud protection CCSW especially encourages novel paradigms and controversial ideas that are not on the above list. The workshop has historically acted as a fertile ground for creative debate and interaction in security-sensitive areas of computing impacted by clouds. This year marked the 13th anniversary of CCSW. In the past decade, CCSW has had a significant impact in our research community.

Title: ConsensusDay '22: ACM Workshop on Developments in Consensus Abstract: ABSTRACTConsensus - loosely defined as global agreement on the state of a decentralised network across its mutually untrusting participants - is an essential ingredient for decentralisation. At the same time, its scalability remains the Achilles' heel of distributed systems. A number of ongoing R&D efforts aim at scaling blockchain networks up to hundreds of thousands of transactions per second. Yet even such performance targets can be seen as modest when the goal is to bring traditional web workloads to the decentralised web (Web3), requiring the handling of billions of transactions per second, large volumes of data, complex workloads and applications, and hard latency requirements. The goal of this workshop is to foster scientific exchange across a wider community in consensus research and adjacent fields, by disseminating and providing a forum for discussion of upcoming impactful research with a practical twist.

Title: AISec '22: 15th ACM Workshop on Artificial Intelligence and Security Abstract: ABSTRACTRecent years have seen a dramatic increase in applications of Artificial Intelligence (AI), Machine Learning (ML), and data mining to security and privacy problems. The analytic tools and intelligent behavior provided by these techniques make AI and ML increasingly important for autonomous real-time analysis and decision making in domains with a wealth of data or that require quick reactions to constantly changing situations. The use of learning methods in security-sensitive domains, in which adversaries may attempt to mislead or evade intelligent machines, creates new frontiers for security research. The recent widespread adoption of "deep learning" techniques, whose security properties are difficult to reason about directly, has only added to the importance of this research. In addition, data mining and machine learning techniques create a wealth of privacy issues, due to the abundance and accessibility of data. The AISec workshop provides a venue for presenting and discussing new developments in the intersection of security and privacy with AI and machine learning.

Title: Checkmate '22: Research on offensive and defensive techniques in the context of Man At The End (MATE) attacks Abstract: ABSTRACTThe MATE (Man-At-The-End) model, in which an attacker has access to the target software and/or hardware environment to be exploited and the ability to observe and modify that environment, poses unique challenges for both defense and offense. The CheckMATE workshop focuses on exploration of both offensive and defensives techniques under this model. CheckMATE will provide a discussion forum for researchers and industrial practitioners that are exploring theorentical, practical, and emperical studies in this interesting area of security.

Title: SCORED '22: ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses Abstract: ABSTRACTRecent attacks on the software supply chain have shed light on the fragility and importance of ensuring the security and integrity of this vital ecosystem. Addressing the technical and social challenges to building trustworthy software for deployment in sensitive and/or large-scale enterprise or governmental settings requires innovative solutions and an interdisciplinary approach. The Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses (SCORED) is a venue that brings together industry practitioners, academics, and policymakers to present and discuss security vulnerabilities, novel defenses against attacks, project demos, adoption requirements and best practices in the software supply chain. The complete SCORED'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3560835

Title: AMSec'22: ACM CCS Workshop on Additive Manufacturing (3D Printing) Security Abstract: ABSTRACTWhile Security is universally needed, it is rarely plug-and-play. The new domain of Additive Manufacturing (a.k.a. 3D Printing) Security requires novel solutions to its unique security concerns. This workshop brings together researchers and practitioners working in this highly inter-disciplinary research field and closely related areas.

Title: DeFi '22: ACM CCS Workshop on Decentralized Finance and Security Abstract: ABSTRACTPowered by blockchains, Decentralized Finance (DeFi) has grown to a significant economy covering exchanges, borrowing/lending, margin trading, derivatives, and more. While DeFi systems are gaining significant traction (e.g., they already manage tens of billions of dollars worth of assets), making them secure has proven exceptionally challenging---a staggering $1.9 billion was stolen in various hacks in the first seven months of 2022 alone.[4] The challenge faced by the research community is twofold. First, DeFi gives rise to new security problems (such as MEV) that existing methods cannot effectively address. Second, understanding DeFi and its security and privacy implications requires knowledge from a wide range of subjects, such as consensus, game theory, programming language, economics, politics, etc. The goal of this workshop is to bring together researchers from many different fields to jointly advance the understanding of DeFi security and develop new methods and solutions leveraging the interdisciplinary expertise of the community.

Title: Net Learning Abstract: Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behavioral features of systems and are more suitable for representing real-world problems. However, the problem of large-scale data analysis has been puzzling the PN field for decades, and thus, limited its universal applicability. In this article, a framework of net learning (NL) is proposed. NL contains the advantages of PN modeling and analysis with the advantages of graph learning computation. Then, two kinds of NL algorithms are designed for performance analysis of stochastic PNs, and more specifically, the hidden feature information of the PN is obtained by mapping net information to the low-dimensional feature space. Experiments demonstrate the effectiveness of the proposed model and algorithms on the performance analysis of stochastic PNs.

Title: Decentralized Control of Minimalistic Robotic Swarms For Guaranteed Target Encapsulation. Abstract: We propose a decentralized control algorithm for a minimalistic robotic swarm with limited capabilities such that the desired global behavior emerges. We consider the problem of searching for and encapsulating various targets present in the environment while avoiding collisions with both static and dynamic obstacles. The novelty of this work is the guaranteed generation of desired complex swarm behavior with constrained individual robots which have no memory, no localization, and no knowledge of the exact relative locations of their neighbors. Moreover, we analyze how the emergent behavior changes with different parameters of the task, noise in the sensor reading, and asynchronous execution.

Title: Constant Approximation of Min-Distances in Near-Linear Time Abstract: In a weighed directed graph $G=(V, E, \omega)$ with m edges and n vertices, we are interested in its basic graph parameters such as diameter, radius and eccentricities, under the nonstandard measure of min-distance which is defined for every pair of vertices $u, v \in V$ as the minimum of the shortest path distances from u to v and from v to u. Similar to standard shortest paths distances, computing graph parameters exactly in terms of min-distances essentially requires $\tilde{\Omega}(m n)$ time under plausible hardness conjectures <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Hence, for faster running time complexities we have to tolerate approximations. Abboud, Vassilevska Williams and Wang [SODA 2016] were the first to study min-distance problems, and they obtained constant factor approximation algorithms in acyclic graphs, with running time $\tilde{O}(m)$ and $\tilde{O}(m \sqrt{n})$ for diameter and radius, respectively. The time complexity of radius in acyclic graphs was recently improved to $\tilde{O}(m)$ by Dalirrooyfard and Kaufmann [ICALP 2021], but at the cost of an $O(\log n)$ approximation ratio. For general graphs, the authors of [DWV+, ICALP 2019] gave the first constant factor approximation algorithm for diameter, radius and eccentricities which runs in time $\tilde{O}(m \sqrt{n})$; besides, for the diameter problem, the running time can be improved to $\tilde{O}(m)$ while blowing up the approximation ratio to $O(\log n)$. A natural question is whether constant approximation and near-linear time can be achieved simultaneously for diameter, radius and eccentricities; so far this is only possible for diameter in the restricted setting of acyclic graphs. In this paper, we answer this question in the affirmative by presenting near-linear time algorithms for all three parameters in general graphs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> As usual, the $\tilde{O}(\cdot)$ notation hides poly-logarithmic factors in n

Title: Verifiable Quantum Advantage without Structure Abstract: We show the following hold, unconditionally unless otherwise stated, relative to a random oracle with probability 1: •There are NP search problems solvable by BQP machines but not BPP machines.•There exist functions that are one-way, and even collision resistant, against classical adversaries but are easily inverted quantumly. Similar separations hold for digital signatures and CPA-secure public key encryption (the latter requiring the assumption of a classically CPA-secure encryption scheme). Interestingly, the separation does not necessarily extend to the case of other cryptographic objects such as PRGs.•There are unconditional publicly verifiable proofs of quantumness with the minimal rounds of interaction: for uniform adversaries, the proofs are non-interactive, whereas for non-uniform adversaries the proofs are two message public coin.•Our results do not appear to contradict the Aaronson-Ambanis conjecture. Assuming this conjecture, there exist publicly verifiable certifiable randomness, again with the minimal rounds of interaction.By replacing the random oracle with a concrete cryptographic hash function such as SHA2, we obtain plausible Minicrypt instantiations of the above results. Previous analogous results all required substantial structure, either in terms of highly structured oracles and/or algebraic assumptions in Cryptomania and beyond.

Title: Separated borders: Exponential-gap fanin-hierarchy theorem for approximative depth-3 circuits Abstract: Mulmuley and Sohoni (2001) proposed an ambitious program, the Geometric Complexity Theory (GCT), to prove $P\neq NP$ and related conjectures using algebraic geometry and representation theory. Gradually, GCT has introduced new structures and questions in complexity. GCT tries to capture the algebraic/geometric notion of ’approximation’ by defining border classes. Surprisingly, (Kumar ToCT’20) proved the universal power of the border of top-fanin- 2 depth-3 circuits $(\overline{\Sigma^{[2]}\Pi\Sigma})$; which is in complete contrast to its classical model. Recently, (Dutta,Dwivedi,Saxena, FOCS’21) put an upper bound, by showing that bounded-top-fanin border depth-3 circuits $(\overline{\Sigma^{[k]}\Pi\Sigma}$ for constant $k)$ can be computed by a polynomial-size algebraic branching program (ABP). It was left open to show an exponential separation between the class of ABPs and $\overline{\Sigma^{[k]}\Pi\Sigma}$. In this article, we show a strongly-exponential separation between any two consecutive border classes, $\overline{\Sigma^{[k]}\Pi\Sigma}$ and $\Sigma^{[k+1]}\Pi\Sigma$, establishing an optimal hierarchy of constant topfanin border depth- 3 circuits. Put in GCT language: we prove an exponential-hierarchy for padded- k-th-secant-varieties of the Chow variety of $\mathbb{F}^{n+1} $. This positively answers [Open question 2 of Dutta,Dwivedi,Saxena FOCS’21] and [Problem 8.10 with constant r, of Landsberg, Annal.Ferrara’15]. Full version: https://www.cse.iitk.ac.in/users/nitin/papers/exphierarchy.pdf

Title: Solving SDP Faster: A Robust IPM Framework and Efficient Implementation Abstract: This paper introduces a new robust interior point method analysis for semidefinite programming (SDP). This new robust analysis can be combined with either logarithmic barrier or hybrid barrier.Under this new framework, we can improve the running time of semidefinite programming (SDP) with variable size $n\times n$ and m constraints up to $\epsilon$ accuracy.We show that for the case $m=\Omega(n^{2})$, we can solve SDPs in $m^{\omega}$ time. This suggests solving SDP is nearly as fast as solving the linear system with equal number of variables and constraints. This is the first result that tall dense SDP can be solved in the nearly-optimal running time, and it also improves the stateof-the-art SDP solver [Jiang, Kathuria, Lee, Padmanabhan and Song, FOCS 2020].In addition to our new IPM analysis, we also propose a number of techniques that might be of further interest, such as, maintaining the inverse of a Kronecker product using lazy updates, a general amortization scheme for positive semi-definite matrices.

Title: Binary Codes with Resilience Beyond 1/4 via Interaction Abstract: In the reliable transmission problem, a sender, Alice, wishes to transmit a bit-string x to a remote receiver, Bob, over a binary channel with adversarial noise. The solution to this problem is to encode x using an error correcting code. As it is long known that the distance of binary codes is at most 1/2, reliable transmission is possible only if the channel corrupts (flips) at most a 1/4-fraction of the communicated bits.We revisit the reliable transmission problem in the two-way setting, where both Alice and Bob can send bits to each other. Our main result is the construction of two-way error correcting codes that are resilient to a constant fraction of corruptions strictly larger than 1/4. Moreover, our code has constant rate and requires Bob to only send one short message. We mention that our result resolves an open problem by Haeupler, Kamath, and Velingker [APPROX-RANDOM, 2015] and by Gupta, Kalai, and Zhang [STOC, 2022].Curiously, our new two-way code requires a fresh perspective on classical error correcting codes: While classical codes have only one distance guarantee for all pairs of codewords (i.e., the minimum distance), we construct codes where the distance between a pair of codewords depends on the “compatibility” of the messages they encode. We also prove that such codes are necessary for our result.

Title: Negative-Weight Single-Source Shortest Paths in Near-linear Time Abstract: We present a randomized algorithm that computes single-source shortest paths (SSSP) in $O\left(m \log ^{8}(n) \log W\right)$ time when edge weights are integral and can be negative. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This essentially resolves the classic negative-weight SSSP problem. The previous bounds are $\tilde{O}\left(\left(m+n^{1.5}\right) \log W\right)$ [BLNPSSSW FOCS’20] and $m^{4 / 3+o(1)} \log W$ [AMV FOCS’20]. Near-linear time algorithms were known previously only for the special case of planar directed graphs [Fakcharoenphol and Rao FOCS’01]. In contrast to all recent developments that rely on sophisticated continuous optimization methods and dynamic algorithms, our algorithm is simple: it requires only a simple graph decomposition and elementary combinatorial tools. In fact, ours is the first combinatorial algorithm for negative-weight SSSP to break through the classic $O(m \sqrt{n} \log W)$ bound from over three decades ago [Gabow and Tarjan SICOMP’89].