arXiv - CS - Cryptography and Security最新文献_第10页

BACKRUNNER: Mitigating Smart Contract Attacks in the Real World BACKRUNNER：缓解现实世界中的智能合约攻击

arXiv - CS - Cryptography and Security Pub Date : 2024-09-10 DOI: arxiv-2409.06213

Chaofan Shou, Yuanyu Ke, Yupeng Yang, Qi Su, Or Dadosh, Assaf Eli, David Benchimol, Doudou Lu, Daniel Tong, Dex Chen, Zoey Tan, Jacob Chia, Koushik Sen, Wenke Lee

{"title":"BACKRUNNER: Mitigating Smart Contract Attacks in the Real World","authors":"Chaofan Shou, Yuanyu Ke, Yupeng Yang, Qi Su, Or Dadosh, Assaf Eli, David Benchimol, Doudou Lu, Daniel Tong, Dex Chen, Zoey Tan, Jacob Chia, Koushik Sen, Wenke Lee","doi":"arxiv-2409.06213","DOIUrl":"https://doi.org/arxiv-2409.06213","url":null,"abstract":"Billions of dollars have been lost due to vulnerabilities in smart contracts.\u0000To counteract this, researchers have proposed attack frontrunning protections\u0000designed to preempt malicious transactions by inserting \"whitehat\" transactions\u0000ahead of them to protect the assets. In this paper, we demonstrate that\u0000existing frontrunning protections have become ineffective in real-world\u0000scenarios. Specifically, we collected 158 recent real-world attack transactions\u0000and discovered that 141 of them can bypass state-of-the-art frontrunning\u0000protections. We systematically analyze these attacks and show how inherent\u0000limitations of existing frontrunning techniques hinder them from protecting\u0000valuable assets in the real world. We then propose a new approach involving 1)\u0000preemptive hijack, and 2) attack backrunning, which circumvent the existing\u0000limitations and can help protect assets before and after an attack. Our\u0000approach adapts the exploit used in the attack to the same or similar contracts\u0000before and after the attack to safeguard the assets. We conceptualize adapting\u0000exploits as a program repair problem and apply established techniques to\u0000implement our approach into a full-fledged framework, BACKRUNNER. Running on\u0000previous attacks in 2023, BACKRUNNER can successfully rescue more than $410M.\u0000In the real world, it has helped rescue over $11.2M worth of assets in 28\u0000separate incidents within two months.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors TERD：防范扩散模型后门的统一框架

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05294

Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang

{"title":"TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors","authors":"Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang","doi":"arxiv-2409.05294","DOIUrl":"https://doi.org/arxiv-2409.05294","url":null,"abstract":"Diffusion models have achieved notable success in image generation, but they\u0000remain highly vulnerable to backdoor attacks, which compromise their integrity\u0000by producing specific undesirable outputs when presented with a pre-defined\u0000trigger. In this paper, we investigate how to protect diffusion models from\u0000this dangerous threat. Specifically, we propose TERD, a backdoor defense\u0000framework that builds unified modeling for current attacks, which enables us to\u0000derive an accessible reversed loss. A trigger reversion strategy is further\u0000employed: an initial approximation of the trigger through noise sampled from a\u0000prior distribution, followed by refinement through differential multi-step\u0000samplers. Additionally, with the reversed trigger, we propose backdoor\u0000detection from the noise space, introducing the first backdoor input detection\u0000approach for diffusion models and a novel model detection algorithm that\u0000calculates the KL divergence between reversed and benign distributions.\u0000Extensive evaluations demonstrate that TERD secures a 100% True Positive Rate\u0000(TPR) and True Negative Rate (TNR) across datasets of varying resolutions. TERD\u0000also demonstrates nice adaptability to other Stochastic Differential Equation\u0000(SDE)-based models. Our code is available at https://github.com/PKU-ML/TERD.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Quality Estimation of True Random Bit-streams 真实随机比特流的高效质量估计

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05543

Cesare Caratozzolo, Valeria Rossi, Kamil Witek, Alberto Trombetta, Massimo Caccia

引用次数: 0

Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning 通过联合交易语言模型和图表示学习检测以太坊欺诈

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.07494

Yifan Jia, Yanbin Wang, Jianguo Sun, Yiwei Liu, Zhang Sheng, Ye Tian

{"title":"Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning","authors":"Yifan Jia, Yanbin Wang, Jianguo Sun, Yiwei Liu, Zhang Sheng, Ye Tian","doi":"arxiv-2409.07494","DOIUrl":"https://doi.org/arxiv-2409.07494","url":null,"abstract":"Ethereum faces growing fraud threats. Current fraud detection methods,\u0000whether employing graph neural networks or sequence models, fail to consider\u0000the semantic information and similarity patterns within transactions. Moreover,\u0000these approaches do not leverage the potential synergistic benefits of\u0000combining both types of models. To address these challenges, we propose\u0000TLMG4Eth that combines a transaction language model with graph-based methods to\u0000capture semantic, similarity, and structural features of transaction data in\u0000Ethereum. We first propose a transaction language model that converts numerical\u0000transaction data into meaningful transaction sentences, enabling the model to\u0000learn explicit transaction semantics. Then, we propose a transaction attribute\u0000similarity graph to learn transaction similarity information, enabling us to\u0000capture intuitive insights into transaction anomalies. Additionally, we\u0000construct an account interaction graph to capture the structural information of\u0000the account transaction network. We employ a deep multi-head attention network\u0000to fuse transaction semantic and similarity embeddings, and ultimately propose\u0000a joint training approach for the multi-head attention network and the account\u0000interaction graph to obtain the synergistic benefits of both.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Framework for Differential Privacy Against Timing Attacks 针对定时攻击的差异隐私框架

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05623

Zachary Ratliff, Salil Vadhan

{"title":"A Framework for Differential Privacy Against Timing Attacks","authors":"Zachary Ratliff, Salil Vadhan","doi":"arxiv-2409.05623","DOIUrl":"https://doi.org/arxiv-2409.05623","url":null,"abstract":"The standard definition of differential privacy (DP) ensures that a\u0000mechanism's output distribution on adjacent datasets is indistinguishable.\u0000However, real-world implementations of DP can, and often do, reveal information\u0000through their runtime distributions, making them susceptible to timing attacks.\u0000In this work, we establish a general framework for ensuring differential\u0000privacy in the presence of timing side channels. We define a new notion of\u0000timing privacy, which captures programs that remain differentially private to\u0000an adversary that observes the program's runtime in addition to the output. Our\u0000framework enables chaining together component programs that are timing-stable\u0000followed by a random delay to obtain DP programs that achieve timing privacy.\u0000Importantly, our definitions allow for measuring timing privacy and output\u0000privacy using different privacy measures. We illustrate how to instantiate our\u0000framework by giving programs for standard DP computations in the RAM and Word\u0000RAM models of computation. Furthermore, we show how our framework can be\u0000realized in code through a natural extension of the OpenDP Programming\u0000Framework.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CipherDM: Secure Three-Party Inference for Diffusion Model Sampling CipherDM：扩散模型采样的安全三方推论

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05414

Xin Zhao, Xiaojun Chen, Xudong Chen, He Li, Tingyu Fan, Zhendong Zhao

{"title":"CipherDM: Secure Three-Party Inference for Diffusion Model Sampling","authors":"Xin Zhao, Xiaojun Chen, Xudong Chen, He Li, Tingyu Fan, Zhendong Zhao","doi":"arxiv-2409.05414","DOIUrl":"https://doi.org/arxiv-2409.05414","url":null,"abstract":"Diffusion Models (DMs) achieve state-of-the-art synthesis results in image\u0000generation and have been applied to various fields. However, DMs sometimes\u0000seriously violate user privacy during usage, making the protection of privacy\u0000an urgent issue. Using traditional privacy computing schemes like Secure\u0000Multi-Party Computation (MPC) directly in DMs faces significant computation and\u0000communication challenges. To address these issues, we propose CipherDM, the\u0000first novel, versatile and universal framework applying MPC technology to DMs\u0000for secure sampling, which can be widely implemented on multiple DM based\u0000tasks. We thoroughly analyze sampling latency breakdown, find time-consuming\u0000parts and design corresponding secure MPC protocols for computing nonlinear\u0000activations including SoftMax, SiLU and Mish. CipherDM is evaluated on popular\u0000architectures (DDPM, DDIM) using MNIST dataset and on SD deployed by diffusers.\u0000Compared to direct implementation on SPU, our approach improves running time by\u0000approximately 1.084times sim 2.328times, and reduces communication costs by\u0000approximately 1.212times sim 1.791times.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Post-Quantum Cryptography on Embedded Systems: A Performance Analysis 评估嵌入式系统上的后量子密码学：性能分析

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05298

Ben Dong, Qian Wang

引用次数: 0

A Taxonomy of Miscompressions: Preparing Image Forensics for Neural Compression 错误压缩分类学：为神经压缩图像取证做准备

arXiv - CS - Cryptography and Security Pub Date : 2024-09-09 DOI: arxiv-2409.05490

Nora Hofer, Rainer Böhme

引用次数: 0

Efficient Homomorphically Encrypted Convolutional Neural Network Without Rotation 无需旋转的高效同态加密卷积神经网络

arXiv - CS - Cryptography and Security Pub Date : 2024-09-08 DOI: arxiv-2409.05205

Sajjad Akherati, Xinmiao Zhang

{"title":"Efficient Homomorphically Encrypted Convolutional Neural Network Without Rotation","authors":"Sajjad Akherati, Xinmiao Zhang","doi":"arxiv-2409.05205","DOIUrl":"https://doi.org/arxiv-2409.05205","url":null,"abstract":"Privacy-preserving neural network (NN) inference can be achieved by utilizing\u0000homomorphic encryption (HE), which allows computations to be directly carried\u0000out over ciphertexts. Popular HE schemes are built over large polynomial rings.\u0000To allow simultaneous multiplications in the convolutional (Conv) and\u0000fully-connected (FC) layers, multiple input data are mapped to coefficients in\u0000the same polynomial, so are the weights of NNs. However, ciphertext rotations\u0000are necessary to compute the sums of products and/or incorporate the outputs of\u0000different channels into the same polynomials. Ciphertext rotations have much\u0000higher complexity than ciphertext multiplications and contribute to the\u0000majority of the latency of HE-evaluated Conv and FC layers. This paper proposes\u0000a novel reformulated server-client joint computation procedure and a new filter\u0000coefficient packing scheme to eliminate ciphertext rotations without affecting\u0000the security of the HE scheme. Our proposed scheme also leads to substantial\u0000reductions on the number of coefficient multiplications needed and the\u0000communication cost between the server and client. For various plain-20\u0000classifiers over the CIFAR-10/100 datasets, our design reduces the running time\u0000of the Conv and FC layers by 15.5% and the communication cost between client\u0000and server by more than 50%, compared to the best prior design.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natias: Neuron Attribution based Transferable Image Adversarial Steganography 纳蒂亚斯基于神经元归因的可转移图像对抗隐写术

arXiv - CS - Cryptography and Security Pub Date : 2024-09-08 DOI: arxiv-2409.04968

Zexin Fan, Kejiang Chen, Kai Zeng, Jiansong Zhang, Weiming Zhang, Nenghai Yu

{"title":"Natias: Neuron Attribution based Transferable Image Adversarial Steganography","authors":"Zexin Fan, Kejiang Chen, Kai Zeng, Jiansong Zhang, Weiming Zhang, Nenghai Yu","doi":"arxiv-2409.04968","DOIUrl":"https://doi.org/arxiv-2409.04968","url":null,"abstract":"Image steganography is a technique to conceal secret messages within digital\u0000images. Steganalysis, on the contrary, aims to detect the presence of secret\u0000messages within images. Recently, deep-learning-based steganalysis methods have\u0000achieved excellent detection performance. As a countermeasure, adversarial\u0000steganography has garnered considerable attention due to its ability to\u0000effectively deceive deep-learning-based steganalysis. However, steganalysts\u0000often employ unknown steganalytic models for detection. Therefore, the ability\u0000of adversarial steganography to deceive non-target steganalytic models, known\u0000as transferability, becomes especially important. Nevertheless, existing\u0000adversarial steganographic methods do not consider how to enhance\u0000transferability. To address this issue, we propose a novel adversarial\u0000steganographic scheme named Natias. Specifically, we first attribute the output\u0000of a steganalytic model to each neuron in the target middle layer to identify\u0000critical features. Next, we corrupt these critical features that may be adopted\u0000by diverse steganalytic models. Consequently, it can promote the\u0000transferability of adversarial steganography. Our proposed method can be\u0000seamlessly integrated with existing adversarial steganography frameworks.\u0000Thorough experimental analyses affirm that our proposed technique possesses\u0000improved transferability when contrasted with former approaches, and it attains\u0000heightened security in retraining scenarios.","PeriodicalId":501332,"journal":{"name":"arXiv - CS - Cryptography and Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0