Convergent Privacy Framework for Multi-layer GNNs through Contractive Message Passing

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Presented at NDSS 2026 by

Zhou Li

Yu Zheng

Chenang Li

Qingsong Wang

www.ndss-symposium.org

Differential privacy (DP) has been integrated into graph neural networks (GNNs) to protect sensitive structural information, e.g., edges, nodes, and associated features across various applications. A prominent approach is to perturb the message-passing process, which forms the core of most GNN architectures. However, existing methods typically incur a privacy cost that grows linearly with the number of layers (e.g., GAP published in Usenix Security’23), ultimately requiring excessive noise to maintain a reasonable privacy level. This limitation becomes particularly problematic when multi-layer GNNs, which have shown better performance than one-layer GNN, are used to process graph data with sensitive information.