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

In recent years, public security incidents caused by deepfake technology have occurred frequently around the world, which makes an efficient and accurate deepfake detection model crucial. The existing advanced methods use the manipulation features in the image to realize the binary classification of real and fake images by training complex neural network models. However, these models rely on a single manipulation feature, and the detection accuracy of these methods will be greatly reduced when the forgery technology or image quality of the training data set and the validation data set are different. Inspired by the existing work, we propose a two-stream collaborative learning framework that combines spatial texture differences and frequency information. The average difference convolution (ADC) is designed to extract the spatial texture difference information of the image, and the gray image frequency-aware decomposition (GFAD) is used to extract the artifact information of the image in the frequency domain. At the same time, the ViT idea is combined with cross attention mechanism for feature fusion to comprehensively mine forged features in forged images. Experimental results show that the proposed model has good detection effects on three benchmark data sets. In terms of cross-dataset evaluation, the AUC on Celeb-DF dataset reaches 82.86%, which is better than the existing advanced methods.

Shuaijv Fang

Zhiyong Zhang

Bin Song

Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

结合纹理差异和频域信息的深度伪造检测模型

The cloud has become pervasive, and we ask: how can we protect cloud data against the cloud itself? For secure user-to-user communication via a cloud server, End-to-End encryption has been formally studied, building on existing TLS channels without requiring new primitives. However, enabling user-to-same-user secure outsourced data storage - solving the analogous problem of “privacy from the server” while (1) relying on existing infrastructure and (2) supporting user mobility, remains open. Existing proposals, like password-protected secret sharing, target the same goal but are incompatible with existing cloud storage services. Specifically, they lack the simplicity needed to directly utilize existing cloud storage without requiring changes on the cloud side. Here, we propose a novel system for securely storing private data in existing cloud storage with the help of a key server (necessary given the requirements). In our system, user data is secure against threats from the cloud server, the key server, and illegitimate users. Only the legitimate user can access the data on any device using a correct passphrase. Most importantly, our system does not require the storage server to support any newly programmable operations. Moreover, leveraging the existing App login, our system requires only one passphrase, which never leaves the user’s device and remains hidden from both servers. The security is proved under formal models, and its efficiency is demonstrated by experiments conducted on Amazon S3. Notably, a preliminary variant, based on our principles, was deployed by Snapchat in their My Eyes Only module, serving hundreds of millions of users!

Long Chen

Ya-Nan Li

Qiang Tang

Moti Yung

End-to-Same-End Encryption: Modularly Augmenting an App with an Efficient, Portable, and Blind Cloud Storage

端到端加密：模块化增强应用程序的高效、便携和盲云存储

Current mobile payment schemes gather detailed information about purchases customers make. This data can then be used to infer a customer’s spending behaviour, potentially violating their privacy. To tackle this problem, we propose an untraceable mobile payment scheme that strikes a better balance, preserving user privacy while allowing the Third-Party Service Provider (TPSP) to collect necessary information such as card details and transaction amount for regulatory compliance. Our scheme offers untraceability for legitimate users from malicious adversaries and curious TPSPs using cryptographic primitives such as partially blind signatures, zero-knowledge proofs and identity-based signatures. It also guarantees that only authorised TPSPs can issue valid payment tokens, and even with limited data the TPSP can still prevent dishonest customers/merchants from double-spending a payment token. We also propose a comprehensive evaluation framework to assess the untraceable payment schemes against seven key criteria such as untraceability, exculpability - merchant double-spending, exculpability - customer double-spending, unforgeability, confidentiality, message authenticity, efficiency and regulatory compliance. We rigorously benchmark the security and privacy of our proposed payment scheme against this framework and other established schemes. Furthermore, we formally verify these properties using complexity-based analysis and Proverif modelling.

Jeyamohan Neera

Xiaomin Chen

Nauman Aslam

Biju Issac

A Trustworthy and Untraceable Centralised Payment Protocol for Mobile Payment

可信且不可追踪的移动支付中心化支付协议

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

Abbreviation	ACM Trans. Priv. Secur.
Journal Impact	2.82
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q2)

ISSN	2471-2566, 2471-2574
h-index	26

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2016，Proportion of original research papers: 100.00%，Self Citation Rate:3.30%， Gold OA Rate: 0.00%

ACM Transactions on Privacy and Security

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Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

End-to-Same-End Encryption: Modularly Augmenting an App with an Efficient, Portable, and Blind Cloud Storage

A Trustworthy and Untraceable Centralised Payment Protocol for Mobile Payment

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

End-to-Same-End Encryption: Modularly Augmenting an App with an Efficient, Portable, and Blind Cloud Storage

Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

A Trustworthy and Untraceable Centralised Payment Protocol for Mobile Payment

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