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cs.LG2023
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…
cs.LG2023
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,…