5 papers
Tight Privacy Audit in One Run
Zihang Xiang, Tianhao Wang, Hanshen Xiao +2
In this paper, we study the problem of privacy audit in one run and show that our method achieves tight audit results for various differentially private protocols. This includes ob…
Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run
Zihang Xiang, Tianhao Wang, Di Wang
Auditing algorithms' privacy typically involves simulating a game-based protocol that guesses which of two adjacent datasets was the original input. Traditional approaches require…
Revisiting Differentially Private Hyper-parameter Tuning
Zihang Xiang, Tianhao Wang, Chenglong Wang +1
We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidat…
Preserving Node-level Privacy in Graph Neural Networks
Zihang Xiang, Tianhao Wang, Di Wang
Differential privacy (DP) has seen immense applications in learning on tabular, image, and sequential data where instance-level privacy is concerned. In learning on graphs, contras…
Differentially Private Non-convex Learning for Multi-layer Neural Networks
Hanpu Shen, Cheng-Long Wang, Zihang Xiang +2
This paper focuses on the problem of Differentially Private Stochastic Optimization for (multi-layer) fully connected neural networks with a single output node. In the first part,…