ACM TRANSACTIONS ON PRIVACY AND SECURITY (TOPS)（前身为 TISSEC）发布信息和系统安全与隐私领域的高质量研究成果。欢迎研究涉及这些领域的各个方面，从技术到系统和应用，再到政策制定。

ACM Transactions on Privacy and Security (TOPS), formerly known as TISSEC, publishes high-quality research findings in the fields of information and system security and privacy. We welcome research covering all aspects of these areas, from technology to systems and applications, as well as policy-making.

《ACM Transactions on Privacy and Security》（前身为 TISSEC）致力于发布信息和系统安全及隐私领域的高质量研究成果。我们欢迎涉及这些领域各个方面的研究，包括技术、系统与应用，以及政策制定。

ACM Transactions on Privacy and Security

Secure multi-party computation has seen substantial performance improvements in recent years and is being increasingly used in commercial products. While a significant amount of work was dedicated to improving its efficiency under standard security models, the threat models do not account for information leakage from the output of secure function evaluation. Quantifying information disclosure about private inputs from observing the function outcome is the subject of this work. Motivated by the City of Boston gender pay gap studies, in this work we focus on the computation of the average of salaries and quantify information disclosure about private inputs of one or more participants (the target) to an adversary via information-theoretic techniques. We study a number of distributions including log-normal, which is typically used for modeling salaries. We consequently evaluate information disclosure after repeated evaluation of the average function on overlapping inputs, as was done in the Boston gender pay study that ran multiple times, and provide recommendations for using the sum and average functions in secure computation applications. Our goal is to develop mechanisms that lower information disclosure about participants’ inputs to a desired level and provide guidelines for setting up real-world secure evaluation of this function.

Alessandro Baccarini

Marina Blanton

Shaofeng Zou

Understanding Information Disclosure from Secure Computation Output: A Comprehensive Study of Average Salary Computation

理解安全计算输出中的信息披露：平均工资计算的综合研究

The prediction module, powered by deep learning models, constitutes a fundamental component of high-level Autonomous Vehicles (AVs). Given the direct influence of the module’s prediction accuracy on AV driving behavior, ensuring its security is paramount. However, limited studies have explored the adversarial robustness of the prediction modules. Furthermore, existing methods still generate adversarial trajectories that deviate significantly from human driving behavior. These deviations can be easily identified as hazardous by AVs’ anomaly detection models and thus cannot effectively evaluate and reflect the robustness of the prediction modules. To bridge this gap, we propose a stealthy and more effective optimization-based attack method. Specifically, we reformulate the optimization problem using Lagrangian relaxation and design a Frenet-based objective function along with a distinct constraint space. We conduct extensive evaluations on 2 popular prediction models and 2 benchmark datasets. Our results show that our attack is highly effective, with over 87% attack success rates, outperforming all baseline attacks. Moreover, our attack method significantly improves the stealthiness of adversarial trajectories while guaranteeing adherence to physical constraints. Our attack is also found robust to noise from upstream modules, transferable across trajectory prediction models, and high realizability. Lastly, to verify its effectiveness in real-world applications, we conduct further simulation evaluations using a production-grade simulator. These simulations reveal that the adversarial trajectory we created could convincingly induce autonomous vehicles (AVs) to initiate hard braking.

Yingkai Dong

Li Wang

Zheng Li

Hao Li

Peng Tang

Chengyu Hu

Shanqing Guo

Safe Driving Adversarial Trajectory Can Mislead: Towards More Stealthy Adversarial Attack Against Autonomous Driving Prediction Module

安全驾驶对抗轨迹可能误导：针对自动驾驶预测模块的更隐蔽对抗攻击研究

Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this paper, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and we show that it is efficient: it takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: it optimizes filter rules for a particular site that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Using AutoFR as a building block, we devise three methodologies that generate filter rules across sites based on: (1) a modified version of AutoFR, (2) rule popularity, and (3) site similarity. We conduct an in-depth comparative analysis of these approaches by considering their effectiveness, efficiency, and maintainability. We demonstrate that some of them can generalize well to new sites in both controlled and live settings. We envision that AutoFR can assist the adblocking community in automatically generating and updating filter rules at scale.

Hieu Le

Salma Elmalaki

Athina Markopoulou

Zubair Shafiq

AutoFR: Automated Filter Rule Generation for Adblocking

AutoFR：自动化广告拦截过滤规则生成

The predominant authentication method still relies on usernames and passwords. To enhance memorability, domain terms may have been opted to include as part of passwords. However, there is little analysis of the extent to which such practice affects password security, so there is a lack of guidance on how users use domain terms on websites with different domain characteristics. To address the problem, we propose a novel approach to analyze the security effect of using domain terms in passwords. The methodology primarily consists of three stages. Firstly, we utilize Web crawlers to harvest domain vocabularies, subsequently leveraging the TextRank algorithm to rank their importance. Afterward, we propose an algorithm for constructing a simulated domain-specific password dataset by replacing password elements with domain terms. Finally, password guessing experiments are done on the dataset using PCFG and Markov model to evaluate the impact of domain terms on password security. The experimental results indicate that, for systems without clear domain, 20% domain terms replacement in the test set can reduce the cracking rate by up to 5.45%. In contrast, for domain-specific systems, 20% domain terms replacement in the training set can increase the cracking rate by 6.45%. These findings provide practical guidance on the application of domain knowledge in password creation for different types of systems. In summary, this study offers a novel perspective for exploring the security implications of passwords influenced by specific domains.

Yubing Bao

Jianping Zeng

Jirui Yang

Ruining Yang

Zhihui Lu

The Effect of Domain Terms on Password Security

领域术语对密码安全的影响

The attacker can generate adversarial examples (AEs) to stealthily mislead automatic speech recognition (ASR) models, raising significant concerns about the security of intelligent voice control (IVC) devices. Existing adversarial attacks mainly generate AEs to mislead ASR models to output specific target English commands (e.g., open the door). However, it remains unknown whether AEs can be used to issue commands in other languages to attack commercial black-box ASR models. In this paper, taking Chinese phrases (e.g., 支付宝付款) and “Chinese-English code-switching” phrases (e.g., 关闭GPS) as the target commands, we propose adversarial attacks for commercial multilingual ASR models. In particular, if a multilingual speech recognition model can recognize Chinese and English, we call it a Chinese-English speech recognition model. In English, the meaning of “支付宝付款” and “关闭GPS” are “Alipay payment” and “turn off GPS”, respectively. In detail, we generate transferable AEs based on the open-sourced conventional DataTang Mandarin ASR model. Given 55 target commands, the success rate for generating AEs of them is up to 96% and 80% for Aliyun ASR API and Tencentyun ASR API, respectively. Our AEs can trigger actual attack actions on voice assistants (e.g., Apple Siri, Xiaomi Xiaoaitongxue) or spread malicious messages through ASR API services, while the target commands in the AEs are inaudible to human beings. Finally, by analyzing the spectrum differences between benign audio clips and AEs, we propose a general defense against adversarial audio attacks.

Xuejing Yuan

Jiangshan Zhang

Kai Chen

Cheng'an Wei

Ruiyuan Li

Zhenkun Ma

Xinqi Ling

Adversarial Attack and Defense for Commercial Black-box Chinese-English Speech Recognition Systems

商用黑盒中英语音识别系统的对抗攻击与防御

短名	ACM Trans. Priv. Secur.
Journal Impact	2.82
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)
期刊索引	SCI Q4中科院 4 区

ISSN	2471-2566, 2471-2574
h-index	26
国内分区	计算机科学(4区)计算机科学计算机信息系统(4区)

涉及主题	计算机科学计算机安全操作系统程序设计语言数学政治学法学工程类计算机网络人工智能生物哲学万维网统计经济地理算法物理历史
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2016，原创研究文献占比: 100.00%，自引率:3.30%， Gold OA占比: 0.00%

ACM Transactions on Privacy and Security

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享

Understanding Information Disclosure from Secure Computation Output: A Comprehensive Study of Average Salary Computation

Safe Driving Adversarial Trajectory Can Mislead: Towards More Stealthy Adversarial Attack Against Autonomous Driving Prediction Module

AutoFR: Automated Filter Rule Generation for Adblocking

The Effect of Domain Terms on Password Security

Adversarial Attack and Defense for Commercial Black-box Chinese-English Speech Recognition Systems

Safe Driving Adversarial Trajectory Can Mislead: Towards More Stealthy Adversarial Attack Against Autonomous Driving Prediction Module

Understanding Information Disclosure from Secure Computation Output: A Comprehensive Study of Average Salary Computation

AutoFR: Automated Filter Rule Generation for Adblocking

Adversarial Attack and Defense for Commercial Black-box Chinese-English Speech Recognition Systems

The Effect of Domain Terms on Password Security