Privacy-preserving Sparse Generalized Eigenvalue Problem. - Lijie Hu; Zihang Xiang; Jiabin Liu; Di Wang 015 (AISTATS, 2023)

A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction. - Hanshen Xiao; Zihang Xiang; Di Wang 015; Srinivas Devadas (SP, 2023)

Differentially Private Non-convex Learning for Multi-layer Neural Networks. - Hanpu Shen; Cheng-Long Wang; Zihang Xiang; Yiming Ying; Di Wang 015 (CoRR, 2023)

Practical Differentially Private and Byzantine-resilient Federated Learning. - Zihang Xiang; Tianhao Wang 001; Wanyu Lin; Di Wang 015 (Proc. ACM Manag. Data, 2023)

Preserving Node-level Privacy in Graph Neural Networks. - Zihang Xiang; Tianhao Wang 001; Di Wang 015 (SP, 2024)

How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning. - Zihang Xiang; Chenglong Wang; Di Wang 015 (CoRR, 2024)

Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects. - Cheng-Long Wang; Qi Li; Zihang Xiang; Di Wang 015 (CoRR, 2024)