DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy

arXiv:2210.04442v2 Announce Type: replace
Abstract: Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose the sensitive information of graphs including both node features and the structure information. In this paper, we aim to achieve node-level differential privacy (DP) for training GNNs so that a node and its edges are protected. Node DP is inherently difficult for GNNs because all direct and multi-hop neighbors …

abstract aim arxiv concerns cs.cr cs.lg data differential differential privacy features gnns graph graph neural networks graphs information networks neural networks node paper privacy structured data success type