Reviewerly/exHarmony huggingface dataset:
TypeSqueezer: When Static Recovery of Function Signatures for Binary Executables Meets Dynamic Analysis

Control-Flow Integrity (CFI) is considered a promising solution in thwarting advanced code-reuse attacks. While the problem of backward-edge protection in CFI is nearly closed, effective forward-edge protection is still a major challenge. The keystone of protecting the forward edge is to resolve indirect call targets, which although can be done quite accurately using type-based solutions given the program source code, it faces difficulties when carried out at the binary level. Since the actual type information is unavailable in COTS binaries, type-based indirect call target matching typically resorts to approximate function signatures inferred using the arity and argument width of indirect callsites and calltargets. Doing so with static analysis, therefore, forces the existing solutions to assume the arity/width boundaries in a too-permissive way to defeat sophisticated attacks.
MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers

Federated Learning (FL) enhances decentralized machine learning by safeguarding data privacy, reducing communication costs, and improving model performance with diverse data sources. However, FL faces vulnerabilities such as untargeted poisoning attacks and targeted backdoor attacks, posing challenges to model integrity and security. Preventing backdoors proves especially challenging due to their stealthy nature. Existing mitigation techniques have shown efficacy but often overlook realistic adversaries and diverse data distributions.
Transformer-based Model for Multi-tab Website Fingerprinting Attack

While the anonymous communication system Tor can protect user privacy, website fingerprinting (WF) attackers can still identify the websites that users access over encrypted network connections by analyzing the metadata generated during network communication. Despite the emergence of new WF attack techniques in recent years, most research in this area has focused on pure traffic traces generated from single-tab browsing behavior. However, multi-tab browsing behavior significantly degrades the performance of WF classification models based on the single-tab assumption. As a result, some research has shifted its focus to multi-tab WF attacks, although most of these works have limited utilization of the mixed information contained in multi-tab traces. In this paper, we propose an end-to-end multi-tab WF attack model, called Transformer-based model for Multi-tab Website Fingerprinting attack (TMWF). Inspired by object detection algorithms in computer vision, we treat multi-tab WF recognition as a problem of predicting ordered sets with a maximum length. By adding enough single-tab queries to the detection model and letting each query extract WF features from different positions in the multi-tab traces, our model's Transformer architecture capitalizes more fully on trace features. Paired with our new proposed model training approach, we accomplish adaptive recognition of multi-tab traces with varying numbers of web pages. This approach successfully eliminates a strong and unrealistic assumption in the field of multi-tab WF attacks - that the number of tabs contained in a sample belongs to the attacker's prior knowledge. Experimental results in various scenarios demonstrate that the performance of TMWF is significantly better than existing multi-tab WF attack models. To evaluate model performance in more authentic scenarios, we present a dataset of multi-tab trace data collected from real open-world environments.
Securing NISQ Quantum Computer Reset Operations Against Higher Energy State Attacks

Enabling the sharing of quantum computers among different users requires a secure reset operation that can reset the state of a qubit to ground state |0> and prevent leakage of the state to a post-reset circuit. This work highlights that the existing reset operations available in superconducting qubit NISQ quantum computers are not fully secure. In particular, this work demonstrates for the first time a new type of higher-energy state attack. Although NISQ quantum computers are typically abstracted as working with only energy states |0> and |1>, this work shows that it is possible for unprivileged users to set the qubit state to |2 or |3>. By breaking the abstraction of a two-level system, the new higher-energy state attack can be deployed to affect the operation of circuits or for covert communication between circuits. This work shows that common reset protocols are ineffective in resetting a qubit from a higher-energy state. To provide a defense, this work proposes a new Cascading Secure Reset (CSR) operation. CSR, without hardware modifications, is able to efficiently and reliably reset higher-energy states back to |0>. CSR achieves a reduction in |3> -initialized state leakage channel capacity by between 1 and 2 orders of magnitude, and does so with a 25x speedup compared with the default decoherence reset.
Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches.
Efficient Multiplicative-to-Additive Function from Joye-Libert Cryptosystem and Its Application to Threshold ECDSA

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.
FutORAMa: A Concretely Efficient Hierarchical Oblivious RAM

Oblivious RAM (ORAM) is a general-purpose technique for hiding memory access patterns. This is a fundamental task underlying many secure computation applications. While known ORAM schemes provide optimal asymptotic complexity, despite extensive efforts, their concrete costs remain prohibitively expensive for many interesting applications. The current state-of-the-art practical ORAM schemes are suitable only for somewhat small memories (Square-Root ORAM or Path ORAM).
FITS: Matching Camera Fingerprints Subject to Software Noise Pollution

Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the <u>fi</u>ngerprint <u>t</u>o <u>s</u>oftware noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise.
Exploration of Power Side-Channel Vulnerabilities in Quantum Computer Controllers

The rapidly growing interest in quantum computing also increases the importance of securing these computers from various physical attacks. Constantly increasing qubit counts and improvements to the fidelity of the quantum computers hold great promise for the ability of these computers to run novel algorithms with highly sensitive intellectual property. However, in today's cloud-based quantum computer setting, users lack physical control over the computers. Physical attacks, such as those perpetrated by malicious insiders in data centers, could be used to extract sensitive information about the circuits being executed on these computers. This work shows the first exploration and study of power-based side-channel attacks in quantum computers. The explored attacks could be used to recover information about the control pulses sent to these computers. By analyzing these control pulses, attackers can reverse-engineer the equivalent gate-level description of the circuits, and the algorithms being run, or data hard-coded into the circuits. This work introduces five new types of attacks, and evaluates them using control pulse information available from cloud-based quantum computers. This work demonstrates how and what circuits could be recovered, and then in turn how to defend from the newly demonstrated side-channel attacks on quantum computing systems.
martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

The development of machine learning models requires a large amount of training data. Data marketplace is a critical platform to trade high-quality and private-domain data that is not publicly available on the Internet. However, as data privacy becomes increasingly important, directly exchanging raw data becomes inappropriate. Federated Learning (FL) is a distributed machine learning paradigm that exchanges data utilities (in form of local models or gradients) among multiple parties without directly sharing the original data. However, we recognize several key challenges in applying existing FL architectures to construct a data marketplace. (i) In existing FL architectures, the Data Acquirer (DA) cannot privately assess the quality of local models submitted by different Data Providers (DPs) prior to trading; (ii)The model aggregation protocols in existing FL designs cannot effectively exclude malicious DPs without "overfitting'' to the DA's (possibly biased) root dataset; (iii) Prior FL designs lack a proper billing mechanism to enforce the DA to fairly allocate the reward according to contributions made by different DPs. To address above challenges, we propose martFL, the first federated learning architecture that is specifically designed to enable a secure utility-driven data marketplace. At a high level, martFL is empowered by two innovative designs: (i) a quality-aware model aggregation protocol that allows the DA to properly exclude local-quality or even poisonous local models from the aggregation, even if the DA's root dataset is biased; (ii) a verifiable data transaction protocol that enables the DA to prove, both succinctly and in zero-knowledge, that it has faithfully aggregated these local models according to the weights that the DA has committed to. This enables the DPs to unambiguously claim the rewards proportional to their weights/contributions. We implement a prototype of martFL and evaluate it extensively over various tasks. The results show that martFL can improve the model accuracy by up to 25% while saving up to 64% data acquisition cost.
FlexiRand: Output Private (Distributed) VRFs and Application to Blockchains

Web3 applications based on blockchains regularly need access to randomness that is unbiased, unpredictable, and publicly verifiable. For Web3 gaming applications, this becomes a crucial selling point to attract more users by providing credibility to the "random reward" distribution feature. A verifiable random function (VRF) protocol satisfies these requirements naturally, and there is a tremendous rise in the use of VRF services. As most blockchains cannot maintain the secret keys required for VRFs, Web3 applications interact with external VRF services via a smart contract where a VRF output is exchanged for a fee. While this smart contract-based plain-text exchange offers the much-needed public verifiability immediately, it severely limits the way the requester can employ the VRF service: the requests cannot be made in advance, and the output cannot be reused. This introduces significant latency and monetary overhead.
HELiKs: HE Linear Algebra Kernels for Secure Inference

We introduce HELiKs, a groundbreaking framework for fast and secure matrix multiplication and 3D convolutions, tailored for privacy-preserving machine learning. Leveraging Homomorphic Encryption (HE) and Additive Secret Sharing, HELiKs enables secure matrix and vector computations while ensuring end-to-end data privacy for all parties. Key innovations of the proposed framework include an efficient multiply-accumulate (MAC) design that significantly reduces HE error growth, a partial sum accumulation strategy that cuts the number of HE rotations by a logarithmic factor, and a novel matrix encoding that facilitates faster online HE multiplications with one-time pre-computation. Furthermore, HELiKs substantially reduces the number of keys used for HE computation, leading to lower bandwidth usage during the setup phase. In our evaluation, HELiKs shows considerable performance improvements in terms of runtime and communication overheads when compared to existing secure computation methods. With our proof-of-work implementation (available on GitHub: https://github.com/shashankballa/HELiKs), we demonstrate state-of-the-art performance with up to 32x speedup for matrix multiplication and 27x speedup for 3D convolution when compared to prior art. HELiKs also reduces communication overheads by 1.5x for matrix multiplication and 29x for 3D convolution over prior works, thereby improving the efficiency of data transfer.
Ramen: Souper Fast Three-Party Computation for RAM Programs

Secure RAM computation allows a number of parties to evaluate a function represented as a random-access machine (RAM) program in a way that reveals nothing about the private inputs of the parties except from what is already revealed by the function output itself. In this work we present Ramen, which is a new protocol for computing RAM programs securely among three parties, tolerating up to one passive corruption. Ramen provides reasonable asymptotic guarantees and is concretely efficient at the same time. We have implemented our protocol and provide extensive benchmarks for various settings.
FetchBench: Systematic Identification and Characterization of Proprietary Prefetchers

Prefetchers speculatively fetch memory using predictions on future memory use by applications. Different CPUs may use different prefetcher types, and two implementations of the same prefetcher can differ in details of their characteristics, leading to distinct runtime behavior. For a few implementations, security researchers showed through manual analysis how to exploit specific prefetchers to leak data. Identifying such vulnerabilities required tedious reverse-engineering, as prefetcher implementations are proprietary and undocumented. So far, no systematic study of prefetchers in common CPUs is available, preventing further security assessment.
Evaluation of Marine Predator Algorithm by Using Engineering Optimisation Problems

This paper provides a real application of a popular swarm-intelligence optimisation method. The aim is to analyse the efficiency of various settings of the marine predator algorithm (MPA). Four crucial numerical parameters of the MPA are statistically analysed to propose the most efficient setting for solving engineering problems. Besides population size, particle velocity parameter P, Lévy flight parameter β, and fish aggregating device (FAD) probabilities are studied. Finally, 193 various settings, including fixed values and dynamic changes of the MPA parameters, are experimentally compared when solving 13 engineering problems. Standard statistical approaches are employed to highlight significant differences in various MPA settings. The setting of two MPA parameters (P, FADs) significantly influences MPA performance. Three newly proposed MPA settings outperform the original variant significantly. The best results provide the MPA variant with the dynamic linear change of P from 0.5 to 0. These parameters influence the velocity of prey and predator individuals in all three stages of the MPA search process. Decreasing the value of P showed that decreasing the velocity of individuals during the search provides good performance. Further, lower efficiency of the MPA with higher FAD values was detected. It means that more occasional use of fish aggregating devices (FADs) can increase the solvability of engineering problems. Regarding population size, lower values (N=10) provided significantly better results compared with the higher values (N=500).
Unhelpful Assumptions in Software Security Research

In the study of software security many factors must be considered. Once venturing beyond the simplest of laboratory experiments, the researcher is obliged to contend with exponentially complex conditions. Software security has been shown to be affected by priming, tool usability, library documentation, organisational security culture, the content and format of internet resources, IT team and developer interaction, Internet search engine ordering, developer personality, security warning placement, mentoring, developer experience and more. In a systematic review of software security papers published since 2016, we have identified a number of unhelpful assumptions that are commonly made by software security researchers. In this paper we list these assumptions, describe why they sometimes do not reflect reality, and suggest implications for researchers.
Chipmunk: Better Synchronized Multi-Signatures from Lattices

Multi-signatures allow for compressing many signatures for the same message that were generated under independent keys into one small aggregated signature. This primitive is particularly useful for proof-of-stake blockchains, like Ethereum, where the same block is signed by many signers, who vouch for the block's validity. Being able to compress all signatures for the same block into a short string significantly reduces the on-chain storage costs, which is an important efficiency metric for blockchains.
Amplification by Shuffling without Shuffling

Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the per-client communication of our protocol only grows sub-linearly in the number of clients. Moreover, we study the concrete efficiency of our protocol and show it can improve per-client communication by one or more orders of magnitude with respect to previous (approximate) shuffling protocols. We also show a differential privacy amplification result for alternating shuffling analogous to the one for uniform shuffling, and demonstrate that shuffling-based protocols for secure summation based a construction of Ishai et al. remain secure under the Alternating Shuffle. In the process we also develop a protocol for exact shuffling in single-server threat model with amortized logarithmic communication per-client which might be of independent interest.
Self-Supervised Clustering Models Based on BYOL Network Structure

Contrastive-based clustering models usually rely on a large number of negative pairs to capture uniform representations, which requires a large batch size and high computational complexity. In contrast, some self-supervised methods perform non-contrastive learning to capture discriminative representations only with positive pairs, but suffer from the collapse of clustering. To solve these issues, a novel end-to-end self-supervised clustering model is proposed in this paper. The basic self-supervised learning network is first modified, followed by the incorporation of a Softmax layer to obtain cluster assignments as data representation. Then, adversarial learning on the cluster assignments is integrated into the methods to further enhance discrimination across different clusters and mitigate the collapse between clusters. To further encourage clustering-oriented guidance, a new cluster-level discrimination is assembled to promote clustering performance by measuring the self-correlation between the learned cluster assignments. Experimental results on real-world datasets exhibit better performance of the proposed model compared with the existing deep clustering methods.
iLeakage: Browser-based Timerless Speculative Execution Attacks on Apple Devices

Over the past few years, the high-end CPU market is undergoing a transformational change. Moving away from using x86 as the sole architecture for high performance devices, we have witnessed the introduction of heavy-weight Arm CPUs computing devices. Among these, perhaps the most influential was the introduction of Apple's M-series architecture, aimed at completely replacing Intel CPUs in the Apple ecosystem. However, while significant effort has been invested analyzing x86 CPUs, the Apple ecosystem remains largely unexplored.
PANIC: PAN-assisted Intra-process Memory Isolation on ARM

Intra-process memory isolation is a well-known technique to enforce least privilege within a process. In this paper, we propose a generic and efficient intra-process memory isolation technique named PANIC, by leveraging Privileged Access Never (PAN) and load/store unprivileged (LSU) instructions on AArch64. PANIC executes process code in kernel mode and compartments code into trusted and untrusted components. The untrusted code is restricted from accessing the isolated memory region, which is located on user pages, and the trusted code is allowed to access the isolated memory region by using LSU instructions. To mitigate threats induced by running user code in kernel mode, PANIC provides two novel security mechanisms: shim-based memory isolation and sensitive instruction emulation. PANIC provides a generic and efficient isolation primitive that can be applied in three different isolation scenarios: protecting sensitive data in CFI, creating isolated execution environments, and hardening JIT code cache. We have implemented a prototype of PANIC and experimental evaluation shows that PANIC incurs very low performance overhead, and performs better than existing methods.