Personalized Privacy-Preserving Graph Neural Network Based on Community Awareness

Existing differentially private graph neural network (GNN) methods predominantly rely on a uniform privacy budget and coarse-grained perturbation mechanisms, implicitly assuming homogeneous privacy sensitivity across graph nodes. However, real-world graphs exhibit pronounced community structures and topological heterogeneity, causing uniform privacy protection to simultaneously under-protect structurally critical regions and over-perturb peripheral ones, thereby degrading both privacy guarantees and model utility. To address this limitation, we propose a community-aware personalized differential privacy (DP) framework for GNNs, which enables fine-grained node-level privacy protection by explicitly leveraging community structure. Specifically, we apply the Louvain algorithm to detect community structures and introduce a community importance evaluation method that integrates structural connectivity with node attribute similarity. Based on these importance scores, we design a personalized privacy budget allocation scheme that adaptively assigns differentiated privacy budgets to nodes under a global (ϵ, δ)-DP constraint. We theoretically prove that the proposed mechanism satisfies node-level (ϵ, δ)-DP, and further demonstrate that it significantly enhances robustness against practical privacy threats such as attribute inference and link reconstruction attacks. Extensive experiments on five real-world graph datasets show that our approach consistently outperforms state-of-the-art DP-GNN baselines across multiple architectures, including GCN, GIN, and GraphSAGE, achieving a superior privacy–utility trade-off.