Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
State Key Laboratory of Blockchain and Security（区块链与安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；Peking University（北京大学）；Finvolution Group（Finvolution集团）
Hanyang Yuan, Jiarong Xu, Cong Wang, Ziqi Yang, Chunping Wang, Keting Yin, Yang Yang
The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.
MISA: Unveiling the Vulnerabilities in Split Federated Learning
Huazhong University of Science and Technology（华中科技大学）；Griffith University（格里菲斯大学）；National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程技术研究中心）；Services Computing Technology and System Lab（服务计算技术与系统实验室）；Hubei Key Laboratory of Distributed System Security（湖北省分布式系统安全重点实验室）；Hubei Engineering Research Center on Big Data Security（湖北省大数据安全工程技术研究中心）；Cluster and Grid Computing Lab（集群与网格计算实验室）
Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network
University of Southern California（南加州大学）；Leonard Davis School of Gerontology, University of Southern California（南加州大学伦纳德·戴维斯老年医学院）；University of Minnesota（明尼苏达大学）
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
School of Computer Science；Technology Soochow University Suzhou China；School of Electronic；Information Engineering Suzhou University of Science；Institute of Artificial Intelligence, Beihang University State Key Laboratory of Complex \& Critical Software Environment Beihang University Beijing China；School of Computing Macquarie University Sydney Australia；Soochow University；Suzhou University of Science；Beihang University；Macquarie University
