Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggregate location time-series. We introduce a game-based definition of the adversarial task, and cast it as a classification problem where machine learning can be used to distinguish whether or not a target user is part of the aggregates.
We empirically evaluate the power of these attacks on both raw and differentially private aggregates using two mobility datasets. We find that membership inference is a serious privacy threat, and show how its effectiveness depends on the adversary's prior knowledge, the characteristics of the underlying location data, as well as the number of users and the timeframe on which aggregation is performed. Although differentially private mechanisms can indeed reduce the extent of the attacks, they also yield a significant loss in utility. Moreover, a strategic adversary mimicking the behavior of the defense mechanism can greatly limit the protection they provide. Overall, our work presents a novel methodology geared to evaluate membership inference on aggregate location data in real-world settings and can be used by providers to assess the quality of privacy protection before data release or by regulators to detect violations.
When Routing Reveals Membership: Privacy Leakage from MoE Router Telemetry
Institute of Science Tokyo（东京科学大学）；Nanyang Technological University（南洋理工大学）；Tohoku University（东北大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Shandong University（山东大学）
E-MIA: Exam-Style Black-Box Membership Inference Attacks against RAG Systems
College of Cyber Security, Jinan University（济南大学网络安全学院）；School of Computer Science, Shanghai Jiaotong University（上海交通大学计算机科学学院）；Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory, Beijing（中关村实验室）
Client-specific Property Inference against Secure Aggregation in Federated Learning
CISPA Helmholtz Center for Information Security（CISPA赫尔姆霍茨信息安全中心）；Department of Networked Systems and Services, CrySyS Lab, BME（布达佩斯经济技术大学网络系统与服务系CrySyS实验室）
Exploring the limits of strong membership inference attacks on large language models
Google DeepMind（谷歌DeepMind）；University College London（伦敦大学学院）；University of Washington（华盛顿大学）；Imperial College London（伦敦帝国学院）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍兹中心）；Stanford University（斯坦福大学）；Microsoft Research（微软研究院）
