Visual privacy attacks and defenses in deep learning: a survey. - Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Andi Zhou; Wanlei Zhou 001 (Artif. Intell. Rev., 2022)

How Does a Deep Learning Model Architecture Impact Its Privacy? - Guangsheng Zhang 004; Bo Liu 001; Huan Tian; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (CoRR, 2022)

FD-MIA: Efficient Attacks on Fairness-enhanced Models. - Huan Tian; Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (CoRR, 2023)

Label-Only Membership Inference Attacks and Defenses in Semantic Segmentation Models. - Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (IEEE Trans. Dependable Secur. Comput., 2023)

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks. - Huan Tian; Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (IJCAI, 2024)

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers. - Guangsheng Zhang 004; Bo Liu 001; Huan Tian; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (USENIX Security Symposium, 2024)

PPFed: A Privacy-Preserving and Personalized Federated Learning Framework. - Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (IEEE Internet Things J., 2024)

RAGLeak: Membership Inference Attacks on RAG-Based Large Language Models. - Kaiyue Feng; Guangsheng Zhang 004; Huan Tian; Heng Xu; Yanjun Zhang; Tianqing Zhu; Ming Ding 001; Bo Liu 001 (ACISP (3), 2025)

Cost-Efficient and Privacy-Preserving Synthesis of Complex Sensitive Data. - Sandeep Suresh; Guangsheng Zhang 004; Bo Liu 001; Barry Drake (ACSW, 2025)

Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers. - Huan Tian; Guangsheng Zhang 004; Bo Liu 001; Tianqing Zhu; Ming Ding 001; Wanlei Zhou 001 (CoRR, 2025)