Achieving Adaptive Privacy-Preserving Graph Neural Networks Training in Cloud Environment

With the widespread adoption of Graph Neural Network (GNN) technology in industry, concerns regarding graph data privacy have become increasingly prominent. Differential privacy has been demonstrated as an effective method to ensure privacy in graph learning. However, existing differential privacy-based GNN methods often overlook the individual privacy protection needs of users, offering uniform privacy guarantees to all. This approach can result in either over-protection or insufficient protection for certain users. To address this issue, we propose an adaptive privacy-preserving GNN training method that accommodates the varying privacy requirements of nodes while achieving high model training accuracy. Specifically, APPGNN allocates adaptive privacy budgets based on individual user privacy needs. Additionally, to mitigate the impact of noise on data utility, APPGNN incorporates a weighted neighborhood aggregation mechanism to enhance GNN model accuracy. Theoretical analysis indicates that APPGNN provides adaptive privacy protection while ensuring ε-differential privacy on node data. Experimental evaluations on four real-world graph datasets validate the effectiveness of APPGNN.