Towards Training Graph Neural Networks with Node-Level Differential Privacy
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Graph Neural Networks (GNNs) have achieved great success in mining graph-structured data. Despite the superior performance of GNNs in learning graph representations, serious privacy concerns have been raised for the trained models which could expose the sensitive information of graphs. We conduct the first formal study of training GNN models to ensure utility while satisfying the rigorous node-level differential privacy considering the private information of both node features and edges. We adopt the training framework utilizing personalized PageRank to decouple the message-passing process from feature aggregation during training GNN models and propose differentially private PageRank algorithms to protect graph topology information formally. Furthermore, we analyze the privacy degradation caused by the sampling process dependent on the differentially private PageRank results during model training and propose a differentially private GNN (DPGNN) algorithm to further protect node features and achieve rigorous node-level differential privacy. Extensive experiments on real-world graph datasets demonstrate the effectiveness of the proposed algorithms for providing node-level differential privacy while preserving good model utility.
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