Edge Private Graph Neural Networks with Singular Value Perturbation

arXiv:2403.10995v1 Announce Type: cross
Abstract: Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN training pipeline has been shown to be vulnerable to node feature leakage and edge extraction attacks. This paper investigates a scenario where an attacker aims to recover private edge information from a trained GNN model. Previous studies have employed differential privacy (DP) to add noise directly to the adjacency …

applications arxiv attacks cs.ai cs.cr cs.lg cs.si data edge extraction feature graph key networks neural networks node pipeline play private role structured data training value vulnerable