Reviewerly/exHarmony huggingface dataset:
Combined Private Circuits - Combined Security Refurbished

Physical attacks are well-known threats to cryptographic implementations. While countermeasures against passive Side-Channel Analysis (SCA) and active Fault Injection Analysis (FIA) exist individually, protecting against their combination remains a significant challenge. A recent attempt at achieving joint security has been published at CCS 2022 under the name CINI-MINIS. The authors introduce relevant security notions and aim to construct arbitrary-order gadgets that remain trivially composable in the presence of a combined adversary. Yet, we show that all CINI-MINIS gadgets at any order are susceptible to a devastating attack with only a single fault and probe due to a lack of error correction modules in the compression. We explain the details of the attack, pinpoint the underlying problem in the constructions, propose an additional design principle, and provide new (fixed) provably secure and composable gadgets for arbitrary order. Luckily, the changes in the compression stage help us to save correction modules and registers elsewhere, making the resulting Combined Private Circuits (CPC) more secure and more efficient than the original ones. We also explain why the discovered flaws have been missed by the associated formal verification tool VERICA (TCHES 2022) and propose fixes to remove its blind spot. Finally, we explore alternative avenues to repair the compression stage without additional corrections based on non-completeness, i.e. constructing a compression that never recombines any secret. Yet, while this approach could have merit for low-order gadgets, it is, for now, hard to generalize and scales poorly to higher orders. We conclude that our refurbished arbitrary order CINI gadgets provide a solid foundation for further research.
Scalable Multiparty Garbling

Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of parties. In this work we present the first global-scale multiparty garbling protocol. The per-party communication complexity of our protocol decreases as the number of parties participating in the protocol increases - for the first time matching the asymptotic communication complexity of non-constant round MPC protocols. Our protocol achieves malicious security in the honest-majority setting and relies on the hardness of the Learning Party with Noise assumption.
A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction

Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed by taking the specified object as the master node and its neighboring objects as the branch nodes. A preprocessing module is applied to exclude unnecessary connections in the graph model based on the speed of the historical trajectory of the object, and to distinguish the features of objects with similar appearances. Feature matching is performed using the Hungarian algorithm, based on the similarity matrix obtained from the features. Post-processing is performed for the temporarily unmatched frames to obtain the final object matching results. The experimental results show that the approach proposed in this paper can achieve the highest MOTA.
Poster: Verifiable Encodings for Maliciously-Secure Homomorphic Encryption Evaluation

Homomorphic encryption has become a promising solution for protecting the privacy of computations on sensitive data. However, existing homomorphic encryption pipelines do not guarantee the correctness of the computation result in the presence of a malicious adversary. In this poster, we present two encodings compatible with state-of-the-art fully homomorphic encryption schemes that enable practical client-verification of homomorphic computations, while enabling all the operations required for modern privacy-preserving analytics. Based on these encodings, we introduce a ready-to-use library for the verification of any homomorphic operation executed over encrypted data. We demonstrate its practicality for various applications and, in particular, we show that it enables verifiability of some homomorphic analytics with less than 3 times overhead compared to the homomorphic encryption baseline.
Correction: Reproducible big data science: A case study in continuous FAIRness

[This corrects the article DOI: 10.1371/journal.pone.0213013.].
Post Quantum Fuzzy Stealth Signatures and Applications

Private payments in blockchain-based cryptocurrencies have been a topic of research, both academic and industrial, ever since the advent of Bitcoin. Stealth address payments were proposed as a solution to improve payment privacy for users and are, in fact, deployed in several major cryptocurrencies today. The mechanism lets users receive payments so that none of these payments are linkable to each other or the recipient. Currently known stealth address mechanisms either (1) are insecure in certain reasonable adversarial models, (2) are inefficient in practice or (3) are incompatible with many existing currencies.
Poster: WIP: Account ZK-Rollups from Sumcheck Arguments

Traditional blockchains execute transactions through the use of consensus mechanisms that guarantee reasonable expectations of integrity and finality. However, often these incur costs making the throughput of transactions orders of magnitude slower than their web2 counterparts. To address this drawback, so called L2 layers offload transactions from the main chain, called L1, execute these fast and anchor back the result of these transaction through succinct checkpoints. ZK-Rollups are emerging as compelling methods to establish the integrity of such checkpoints, by the use of compressed cryptographic proofs.
Jack-in-the-box: An Empirical Study of JavaScript Bundling on the Web and its Security Implications

In recent years, we have seen an increased interest in studying the software supply chain of user-facing applications to uncover problematic third-party dependencies. Prior work shows that web applications often rely on outdated or vulnerable third-party code. Moreover, real-world supply chain attacks show that dependencies can also be used to deliver malicious code, e.g., for carrying cryptomining operations. Nonetheless, existing measurement studies in this domain neglect an important software engineering practice: developers often merge together third-party code into a single file called bundle, which they then deliver from their own servers, making it appear as first-party code. Bundlers like Webpack or Rollup are popular open-source projects with tens of thousand of GitHub stars, suggesting that this technology is widely-used by developers. Ignoring bundling may result in underestimating the complexity of modern software supply chains.
Activation extending based on long-range dependencies for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 76.6% and 77.4% mIoU in val and test sets, which is superior to the comparison baselines.
Poster: Ethics of Computer Security and Privacy Research - Trends and Standards from a Data Perspective

Ethics is an important criterion for security research. This work presents the current status and trends that security researchers have taken to address ethical concerns in their studies from a data perspective. In particular, we created a dataset of 3,756 papers published in three top-tier conferences between 2010 and 2022, among which 963 papers were identified with ethical concerns. With this dataset, we provided answers to three questions regarding the current practices and trends: (1) What is the landscape of ethical considerations in security research? For example, how many security research projects have raised ethical concerns in their studies, and which research areas are likely to cause ethical risks and concerns? (2) What are the current practices to address these ethical risks? And (3) What are the important factors impacting the ethical awareness of researchers?
Poster: Fooling XAI with Explanation-Aware Backdoors

The overabundance of learnable parameters in recent machine-learning models renders them inscrutable. Even their developers can not explain their exact inner workings anymore. For this reason, researchers have developed explanation algorithms to shed light on a model's decision-making process. Explanations identify the deciding factors for a model's decision. Therefore, much hope is set in explanations to solve problems like biases, spurious correlations, and more prominently attacks like neural backdoors.
Comparing Derivatives of Neural Networks for Regression

Abstract In the past decades, neural networks have rapidly grown in popularity as a way to model complex non-linear relationships. The computational efficiently and flexibility of neural networks has made them popular for machine learning-based optimization methods. As such the derivative of a neural network’s output is required for gradient-based optimization algorithms. Recently, there have been several works towards improving derivatives of neural network targets, however there is yet to be done a comparative study on the different derivation methods for the derivative of a neural network’s targets with respect to its input features. Consequently, this paper’s objective is to implement and compare common methods for obtaining or approximating the derivative of neural network targets with respect to their inputs. The methods studied include analytical derivatives, finite differences, complex step approximation, and automatic differentiation. The methods are tested by training deep multilayer perceptrons for regression with several analytical functions. The derivatives of the neural network-derived methods are evaluated against the exact derivative of the test functions. Results show that all of the derivation methods provide the same derivative approximation to near working precision of the computer. Implementation of the study is done using the TensorFlow library in a provided Python code.
Poster: A Privacy-Preserving Smart Contract Vulnerability Detection Framework for Permissioned Blockchain

The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms, etc. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.
Poster: Metadata-private Messaging without Coordination

Metadata-private messaging (MPM) refers to an end-to-end encrypted messaging system that protects not just the payload messages but also the privacy-revealing communication metadata, such as user identities, conversation frequencies, traffic volumes, etc. Protecting the communication metadata is challenging due to the existence of global adversaries that can monitor and even actively interfere with the traffic. Established systems like Tor are not adequate under such adversarial models. Thus, many academic systems have been proposed to push this frontier with different trade-offs among security, performance, and trust assumptions. Despite progress, one major limitation prevalent in almost all prior art is the requirement for messaging buddies to coordinate the time (also known as "dialing'') to start the conversation. Compared to traditional messaging systems, such coordination protocols, which must also be metadata private, are expensive for both user adoption and service operations. In this ongoing study, we propose to develop a new MPM system without coordination. Unlike prior art, we plan to model the MPM system into two separate modules: metadata-private notifications and metadata-private message retrieval, which is intuitively inspired by traditional messaging systems. We will instantiate these ideas by drawing insights from recent work about private signaling, oblivious message retrieval, and MPM under hardware trust.
Poster: Multi-target &amp; Multi-trigger Backdoor Attacks on Graph Neural Networks

Recent research has indicated that Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, and existing studies focus on the One-to-One attack where there is a single target triggered by a single backdoor. In this work, we explore two advanced backdoor attacks, i.e., the multi-target and multi-trigger backdoor attacks, on GNNs: 1) One-to-N attack, where there are multiple backdoor targets triggered by controlling different values of the trigger; 2) N-to-One attack, where the attack is only triggered when all the N triggers are present. The initial experimental results illustrate that both attacks can achieve a high attack success rate (up to 99.72%) on GNNs for the node classification task.
ASHES '23: Workshop on Attacks and Solutions in Hardware Security

The workshop on "Attacks and Solutions in HardwarE Security (ASHES)" welcomes any theoretical and practical works on hardware security, including attacks, solutions, countermeasures, proofs, classification, formalization, and implementations. Besides mainstream research, ASHES puts some focus on new and emerging scenarios: This includes the Internet of Things (IoT), nuclear weapons inspections, arms control, consumer and infrastructure security, or supply chain security, among others. ASHES also welcomes works on special purpose hardware, such as lightweight, low-cost, and energy-efficient devices, or non-electronic security systems.
Poster: Towards Lightweight TEE-Assisted MPC

This work presents HPCG (short for hardware-assisted pseudorandom correlation generator), a work-in-progress lightweight TEE (LTEE)-assisted MPC solution for both high performance and strong security. HPCG relies on a succinct codebase and small LTEE chips that work only in the MPC offline phase, with the aim of addressing efficiency bottlenecks in traditional MPC, while minimizing the use and trust in secure hardware, which makes a rational compromise between pure cryptography and TEE techniques. We design HPCG to work for diverse MPC settings in the preprocessing model and conform to the mainstream secret-sharing semantics, making it easy to deploy and integrate into existing MPC practices.
SCORED '23: Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses

Recent 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'23 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3576915
Tutorial-HEPack4ML '23: Advanced HE Packing Methods with Applications to ML

Outsourcing computations over sensitive data to a third-party cloud environment should often rely on dedicated privacy-preserving solutions in order to adhere to privacy regulations such as the GDPR [7]. One solution that gained great attention is fully homomorphic encryption (FHE), a cryptographic method that allows performing different types of computation on encrypted data. Still, writing a non-interactive FHE code that evaluates complex functions is a task that is mostly left to experts. Otherwise, the resulted code may become very slow and even impractical.
On the Connectedness of the Topology Optimization Predictors

Abstract Deep learning-based topology optimization predictors have been shown to be effective in generating optimal designs. However, these predictors are prone to topological errors, particularly for high-resolution domains. Although various methods have been developed to enhance the accuracy of predicted structures, such as using large training datasets, complex networks, and physics-based loss functions, they do not include topological metrics in the deep learning models. Similar issues arise in other applications, such as blood vessels, neurons, or road segmentation from images, and several modifications to typical loss functions have been proposed to improve the topological validity of the predictions. In this study, we evaluate and compare four distinct topological loss functions to explore their influence on the performance of deep learning-based topology optimization predictors. Our findings offer insights into the advantages and limitations of these modified loss functions and provide a basis for future research and development aimed at improving the accuracy and efficiency of deep learning predictors in topology optimization.
Poster: Secure and Differentially Private kth Ranked Element

The problem of finding the kth Ranked Element (KRE) is of particular interest in collaborative studies for financial and medical agencies alike. Many of the applications of KRE deal with sensitive information that needs to be protected. The protocol by Chandran et al. (SECRYPT'22) considers a model where multiple parties hold datasets with many elements and wish to compute the kth element of their joint dataset. In their model, all participating parties interact with a central party in a star network topology. However, they leak some intermediate information to the central party.
Poster: Signer Discretion is Advised: On the Insecurity of Vitalik's Threshold Hash-based Signatures

We show that the Lamport threshold signature scheme proposed by Vitalik Buterin is not existentially unforgeable under chosen message attacks (EU-CMA). In this work, we formalize the proposed threshold hash-based signature scheme, and show an attack that results in a 60-bit security reduction. Our attack completes in seconds in a setting with a single malicious adversary (the leader of a consensus round), thus contradicting the claim that even with 96 malicious colluding participants (out of a total of 256), an adversary can only make a signature for approximately 1 in 280 possible values. In summary, the original estimated security analysis of the proposed threshold signature scheme claimed security against an adversary in control of approximately a year of continuous work from the entire bitcoin network. Our attack, however, runs in seconds using a commodity laptop.
WAHC '23: 11th Workshop on Encrypted Computing &amp; Applied Homomorphic Cryptography

The 11th Workshop on Encrypted Computing and Applied Homomorphic Cryptography is held in Copenhagen, Denmark on Novem- ber 26, 2023, 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 9 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 one invited talk.
<u>T</u> unne <u>L</u> s for <u>B</u> ootlegging: Fully Reverse-Engineering GPU TLBs for Challenging Isolation Guarantees of NVIDIA MIG

Recent studies have revealed much detailed information about the translation lookaside buffers (TLBs) of modern CPUs, but we find that many properties of such components in modern GPUs still remain unknown or unclear. To fill this knowledge gap, we develop a new GPU TLB reverse-engineering method and apply it to a variety of consumer- and server-grade GPUs in Turing and Ampere generations. Aside from learning significantly more comprehensive and accurate GPU TLB properties, we discover a design flaw of NVIDIA Multi-Instance GPU (MIG) feature. MIG claims full partitioning of the entire GPU memory system for secure GPU sharing in cloud computing. However, we surprisingly find that MIG does not partition the last-level TLB, which is shared by all the compute units in a GPU. Exploiting this design flaw and learned TLB properties, we are able to construct a covert channel for data exfiltration across MIG-enforced isolation. To the best of our knowledge, this is the first attack on MIG. We evaluate the proposed attack on a commercial cloud platform, and we successfully achieve reliable data exfiltration from a victim tenant at a speed of up to 31 kbps with a very high accuracy around 99.8%. Even when the victim is using the GPU for deep neural network training, the transmission can still reach more than 25 kbps with a more than 99.5% accuracy. We propose and implement a mitigation approach that can effectively thwart data exfiltration through this covert channel. Additionally, we present a preliminary study on exploiting the access patterns of the last-level TLB to infer the identity of applications running in other MIG-created GPU instances.