Differentially Private Graph Neural Networks for Graph Classification and Its Adaptive Optimization

Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data. Despite the successful application of Differential Privacy Stochastic Gradient Descent (DP-SGD) in deep learning, its adaptation to GNNs presents challenges due to the complexities inherent in GNN architectures and their distinctive message-passing mechanism. To solve the problem, this study delves into the challenges encountered when implementing DP-SGD in GNNs (DPGNNs), focusing on factors associated with the GNN model including the choice of learning rates, GNN frameworks, batch size, and gradient clipping thresholds, alongside parameters related to the Differential Privacy (DP) algorithm such as sensitivity, dataset size, privacy budget. The study reveals the impact of these factors on the performance of DPGNNs and provides recommendations for parameter values. Furthermore, we propose two adaptive optimization strategies to address the unique effects of learning rate and privacy budget on DPGNNs: adaptive learning rates and adaptive noise scales. These adaptive strategies automate parameter tuning, eliminating complex manual adjustments, significantly reducing training time and energy consumption, while achieving an optimal balance between privacy protection and model performance enhancement. Extensive experiments on graph classification tasks across multiple real-world datasets have validated the effectiveness of these strategies, significantly improving the classification accuracy and robustness of DPGNNs while ensuring data privacy.