Reviewerly/exHarmony huggingface dataset:
"I just stopped using one and started using the other": Motivations, Techniques, and Challenges When Switching Password Managers

This paper explores what motivates password manager (PM) users in the US to switch from one PM to another, the techniques they employ when switching, and challenges they encounter throughout. Through a screener (n = 412) followed by a main survey (n = 54), we find that browser-based PMs are the most widely used, with most of these users motivated to use the PM due to convenience. Unfortunately, password reuse remains high. Most participants that switch PMs do so for usability reasons, but are also motivated by cost, as third-party PMs' full suite of features often require a subscription fee. Some PM-switchers are also motivated by recent security breaches, such as what was reported at LastPass in the Fall of 2022, with some participants losing trust in LastPass and PMs generally as a result. Those that switch mostly employ manual techniques of moving their passwords, e.g., copying and pasting their credentials from their previous to their new PM, despite most PMs offering ways to automatically transfer credentials in bulk across PMs. Assistance during the switching process is limited, with less than half of participants that switched receiving guidance during the switching process. From these findings, we make recommendations to PMs that can improve their overall user experience and use, including eliciting and acting on regular feedback from users as well as making PM settings more easily reachable and customizable by end-users.
FPT: A Fixed-Point Accelerator for Torus Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a technique that allows computation on encrypted data. It has the potential to drastically change privacy considerations in the cloud, but high computational and memory overheads are preventing its broad adoption. TFHE is a promising Torus-based FHE scheme that heavily relies on bootstrapping, the noise-removal tool invoked after each encrypted logical/arithmetical operation.
On the Security of Rate-limited Privacy Pass

The privacy pass protocol allows users to redeem anonymously issued cryptographic tokens instead of solving annoying CAPTCHAs. The issuing authority verifies the credibility of the user, who can later use the pass while browsing the web using an anonymous or virtual private network. Hendrickson et al. proposed an IETF draft (privacypass-rate-limit-tokens-00) for a rate-limiting version of the privacy pass protocol, also called rate-limited Privacy Pass(RlP). Introducing a new actor called a mediator makes both versions inherently different. The mediator applies access policies to rate-limit users' access to the service while, at the same time, should be oblivious to the website/origin the user is trying to access. In this paper, we formally define the rate-limited Privacy Pass protocol and propose a game-based security model to capture the informal security notions introduced by Hendrickson et al.. We show a construction from simple building blocks that fulfills our security definitions and even allows for a post-quantum secure instantiation. Interestingly, the instantiation proposed in the IETF draft is a specific case of our construction. Thus, we can reuse the security arguments for the generic construction and show that the version used in practice is secure.
How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting Governance

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers.
Lost along the Way: Understanding and Mitigating Path-Misresolution Threats to Container Isolation

Filesystem isolation enforced by today's container technology has been found to be less effective in the presence of host-container interactions increasingly utilized by container tools. This weakened isolation has led to a type of path misresolution (Pamir) vulnerabilities, which have been considered to be highly risky and continuously reported over the years. In this paper, we present the first systematic study on the Pamir risk and the existing fixes to related vulnerabilities. Our research reveals that in spite of significant efforts being made to patch vulnerable container tools and address the risk, the Pamir vulnerabilities continue to be discovered, including a new vulnerability (CVE-2023-0778) we rediscovered from patched software. A key insight of our study is that the Pamir risk is inherently hard to prevent at the level of container tools, due to their heavy reliance on third-party components. While security inspections should be applied to all components to mediate host-container interactions, third-party component developers tend to believe that container tools should perform security checks before invoking their components, and are therefore reluctant to patch their code with the container-specific protection. Moreover, due to the large number of components today's container tools depend on, re-implementing all of them is impractical.
Poster: Boosting Adversarial Robustness by Adversarial Pre-training

Vision Transformer (ViT) shows superior performance on various tasks, but, similar to other deep learning techniques, it is vulnerable to adversarial attacks. Due to the differences between ViT and traditional CNNs, previous works designed new adversarial training methods as defenses according to the design of ViT, such as blocking attention to individual patches or dropping embeddings with low attention. However, these methods usually focus on fine-tuning stage or the training of the model itself. Improving robustness at the pre-training stage, especially with lower overhead, has yet to be thoroughly investigated. This paper proposes a novel method, Adv-MAE, which increases adversarial robustness by masked adversarial pre-training without a penalty to performance on clean data. We design a simple method to generate adversarial perturbation for the autoencoder, as the autoencoder does not provide classification results. Then, we use masked inputs with perturbation to conduct adversarial training for the autoencoder. The pre-trained autoencoder can be used to build a ViT with better robustness. Our experimental results show that, when using adversarial fine-tuning, Adv-MAE offers better accuracy under adversarial attack than the non-adversarial pre-training method (3.46% higher on CIFAR-10, 1.12% higher on Tiny ImageNet). It also shows better accuracy on clean data (4.94% higher on CIFAR-10, 1.74% higher on Tiny ImageNet), meaning Adv-MAE does not deteriorate performance on clean inputs. In addition, masked pre-training also shows much lower time consumption at each training epoch.
Linear Communication in Malicious Majority MPC

The SPDZ multiparty computation protocol \citeC:DPSZ12 allows n parties to securely compute arithmetic circuits over a finite field, while tolerating up to n-1 active corruptions. A line of work building upon SPDZ has made considerable improvement to the protocol's performance, typically focusing on concrete efficiency. However, the communication complexity of each of these protocols is Ømega(n^2|C|).
Passive SSH Key Compromise via Lattices

We demonstrate that a passive network attacker can opportunistically obtain private RSA host keys from an SSH server that experiences a naturally arising fault during signature computation. In prior work, this was not believed to be possible for the SSH protocol because the signature included information like the shared Diffie-Hellman secret that would not be available to a passive network observer. We show that for the signature parameters commonly in use for SSH, there is an efficient lattice attack to recover the private key in case of a signature fault. We provide a security analysis of the SSH, IKEv1, and IKEv2 protocols in this scenario, and use our attack to discover hundreds of compromised keys in the wild from several independently vulnerable implementations.
Poster: Mujaz: A Summarization-based Approach for Normalized Vulnerability Description

This work proposes a multi-task Natural Language Processing (NLP) system to normalize and summarize the descriptions into a uniform structure. A dataset was curated from an official public database and broken into several constituent entities representing a particular aspect of the description. A model is trained on the annotated features independently and jointly to generate a simple and uniform summary. We also introduce our human metrics to judge the quality of the generated summary with respect to human comprehension and content accuracy.
ELEKTRA: Efficient Lightweight multi-dEvice Key TRAnsparency

Key Transparency (KT) systems enable service providers of end-to-end encrypted communication (E2EE) platforms to maintain a Verifiable Key Directory (VKD) that maps each user's identifier, such as a username or email address, to their identity public key(s). Users periodically monitor the directory to ensure their own identifier maps to the correct keys, thus detecting any attempt to register a fake key on their behalf to Meddler-in-the-Middle (MitM) their communications.
Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling

Because of the rapid rise in the count of corporations using cloud-dependent infrastructure as the foundation for big data storing and analysis. The fundamental difficulty in scheduling big data services in cloud-dependent systems is ensuring the shortest possible makespan while simultaneously reducing the number of resources being used. We have created a new, secure map reduce scheduling method that functions as follows. Initially, the cloud architecture is designed and the tasks are generated. In the pre-processing phase, the huge set of tasks was processed by the map-reduce scheduling framework. Afterward, the optimal task scheduling task is conducted which utilizes a hybrid algorithm named Tunicate Combined Moth Flame Algorithm (TCMFA), which provides better task scheduling via providing optimal makespan, execution time, and security. This proposed TCMFA is the hybridization of both Moth Flame Optimization (MFO) and Tunicate Swarm Algorithm (TSA). The error rate of the TCMFA gets reduced to 320 approximately over other conventional methods such as RSA, ACO, GHO, BTS, OWPSO, BES, PRO, SOA, COOT, TSA & MFO which proves the accuracy of our TCMFA and makes it more efficient and secure for optimal map-reduce scheduling.
Securely Sampling Discrete Gaussian Noise for Multi-Party Differential Privacy

Differential Privacy (DP) is a widely used technique for protecting individuals' privacy by limiting what can be inferred about them from aggregate data. Recently, there have been efforts to implement DP using Secure Multi-Party Computation (MPC) to achieve high utility without the need for a trusted third party. One of the key components of implementing DP in MPC is noise sampling. Our work presents the first MPC solution for sampling discrete Gaussian, a common type of noise used for constructing DP mechanisms, which plays nicely with malicious secure MPC protocols.
Short Privacy-Preserving Proofs of Liabilities

In the wake of fraud scandals involving decentralized exchanges and the significant financial loss suffered by individuals, regulators are pressed to put mechanisms in place that enforce customer protections and capital requirements in decentralized ecosystems. Proof of liabilities (PoL) is such a mechanism: it allows a prover (e.g., an exchange) to prove its liability to a verifier (i.e., a customer).
Intersemiotic Digital Legal Translation

In recent years, digital devices have become fundamental tools for intersemiotic translation. Until recently, legal acts were mainly implemented through words and paper documents; we now can digitalize these activities, and new technological advancements and tools can assist in conveying and facilitating intersemiotic translation. Both developers and users can access a range of semiotic resources with the help of such technologies. In particular, it is possible to use new tools to transpose into digital form and perform legally relevant acts via these channels. The chapter will present an analysis of intersemiotic legal translation in the context of the development of digital platforms, as well as the intersemiotic translations deriving from the use of such platforms.
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation

Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over decentralized nodes, enabling collaboration with an untrusted server to train GNNs without revealing the existence of any link. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology using Bayesian estimation, alleviating the negative impact of LDP on the accuracy of the trained GNNs. We bound the mean absolute error of the inferred link probabilities against the ground truth graph topology. We then propose two variants of our LDP mechanism complementing each other in different privacy settings, one of which estimates fewer links under lower privacy budgets to avoid false positive link estimates when the uncertainty is high, while the other utilizes more information and performs better given relatively higher privacy budgets. Furthermore, we propose a hybrid variant that combines both strategies and is able to perform better across different privacy budgets. Extensive experiments show that our approach outperforms existing methods in terms of accuracy under varying privacy budgets.
Concentrated Geo-Privacy

This paper proposes concentrated geo-privacy (CGP), a privacy notion that can be considered as the counterpart of concentrated differential privacy (CDP) for geometric data. Compared with the previous notion of geo-privacy [1,5], which is the counterpart of standard differential privacy, CGP offers many benefits including simplicity of the mechanism, lower noise scale in high dimensions, and better composability known as advanced composition. The last one is the most important, as it allows us to design complex mechanisms using smaller building blocks while achieving better utilities. To complement this result, we show that the previous notion of geo-privacy inherently does not admit advanced composition even using its approximate version. Next, we study three problems on private geometric data: the identity query, k nearest neighbors, and convex hulls. While the first problem has been previously studied, we give the first mechanisms for the latter two under geo-privacy. For all three problems, composability is essential in obtaining good utility guarantees on the privatized query answer.
Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing

Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications - e.g., machine learning or image processing - are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline.
Fast Unbalanced Private Set Union from Fully Homomorphic Encryption

Private set union (PSU) allows two parties to compute the union of their sets without revealing anything else. It has been widely used in various applications. While several computationally efficient PSU protocols have been developed for the balanced case, they have a potential limitation in their communication complexity, which grows (super)-linearly with the size of the larger set. This poses a challenge when performing PSU in the unbalanced setting, where one party is a constrained device holding a small set, and another is a service provider holding a large set.
Concurrent Security of Anonymous Credentials Light, Revisited

We revisit the concurrent security guarantees of the well-known Anonymous Credentials Light (ACL) scheme (Baldimtsi and Lysyanskaya, CCS'13). This scheme was originally proven secure when executed sequentially, and its concurrent security was left as an open problem. A later work of Benhamouda et al. (EUROCRYPT'21) gave an efficient attack on ACL when executed concurrently, seemingly resolving this question once and for all.
CheckMate: Automated Game-Theoretic Security Reasoning

We present the CheckMate framework for full automation of game-theoretic security analysis, with particular focus on blockchain technologies. CheckMate analyzes protocols modeled as games for their game-theoretic security - that is, for incentive compatibility and Byzantine fault-tolerance. The framework either proves the protocols secure by providing defense strategies or yields all possible attack vectors. For protocols that are not secure, CheckMate can also provide weakest preconditions under which the protocol becomes secure, if they exist. CheckMate implements a sound and complete encoding of game-theoretic security in first-order linear real arithmetic, thereby reducing security analysis to satisfiability solving. CheckMate further automates efficient handling of case splitting on arithmetic terms. Experiments show CheckMate scales, analyzing games with trillions of strategies that model phases of Bitcoin's Lightning Network.
SalsaPicante: A Machine Learning Attack on LWE with Binary Secrets

Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST [13] is based on module LWE [2], and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE-based PQ cryptosystems is critical, but certain implementation choices could weaken them. One such choice is sparse binary secrets, desirable for PQ HE schemes for efficiency reasons. Prior work SALSA[51] demonstrated a machine learning-based attack on LWE with sparse binary secrets in small dimensions (n ≤ = 128) and low Hamming weights (h ≤ = 4). However, this attack assumes access to millions of eavesdropped LWE samples and fails at higher Hamming weights or dimensions.
SpecVerilog: Adapting Information Flow Control for Secure Speculation

To address transient execution vulnerabilities, processor architects have proposed both defensive designs and formal descriptions of the security they provide. However, these designs are not typically formally proven to enforce the claimed guarantees; more importantly, there are few tools to automatically ensure that Register Transfer Level (RTL) descriptions are faithful to high-level designs.
Secure and Timely GPU Execution in Cyber-physical Systems

Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems (CPSs), frequently used to perform real-time safety-critical functions, such as object detection on autonomous vehicles. As a result, availability is important for GPU tasks in CPS platforms. However, existing Trusted Execution Environments (TEE) solutions with availability guarantees focus only on CPU computing.
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks

Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users. As local training data comes from different users who may not be trustworthy, several studies have shown that FL is vulnerable to poisoning attacks. Meanwhile, to protect the privacy of local users, FL is usually trained in a differentially private way (DPFL). Thus, in this paper, we ask: What are the underlying connections between differential privacy and certified robustness in FL against poisoning attacks? Can we leverage the innate privacy property of DPFL to provide certified robustness for FL? Can we further improve the privacy of FL to improve such robustness certification? We first investigate both user-level and instance-level privacy of FL and provide formal privacy analysis to achieve improved instance-level privacy. We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels. Theoretically, we provide the certified robustness of DPFL based on both criteria given a bounded number of adversarial users or instances. Empirically, we conduct extensive experiments to verify our theories under a range of poisoning attacks on different datasets. We find that increasing the level of privacy protection in DPFL results in stronger certified attack inefficacy; however, it does not necessarily lead to a stronger certified prediction. Thus, achieving the optimal certified prediction requires a proper balance between privacy and utility loss.
A Thorough Evaluation of RAMBAM

The application of masking, widely regarded as the most robust and reliable countermeasure against Side-Channel Analysis~(SCA) attacks, has been the subject of extensive research across a range of cryptographic algorithms, especially AES. However, the implementation cost associated with applying such a countermeasure can be significant and even in some scenarios infeasible due to considerations such as area and latency overheads, as well as the need for fresh randomness to ensure the security properties of the resulting design. Most of these overheads originate from the ability to maintain security in the presence of physical defaults such as glitches and transitions. Among several schemes with a trade-off between such overheads, RAMBAM, presented at CHES~2022, offers an ultra-low latency in terms of the number of clock cycles. It is dedicated to the AES and utilizes redundant representations of the finite field elements to enhance protection against both passive and active physical attacks.
Phoenix: Detect and Locate Resilience Issues in Blockchain via Context-Sensitive Chaos

Resilience is vital to blockchain systems and helps them automatically adapt and continue providing their service when adverse situations occur, e.g., node crashing and data discarding. However, due to the vulnerabilities in their implementation, blockchain systems may fail to recover from the error situations, resulting in permanent service disruptions. Such vulnerabilities are called resilience issues.