Integrating adaptive differential privacy in graph convolution network for graph classification tasks

Graph Neural Networks (GNNs) have demonstrated strong practical value in graph classification tasks. However, graph data often contains sensitive information, making privacy protection an urgent practical need. Traditional privacy protection technologies have key limitations: anonymization is prone to re-identification via auxiliary knowledge, secure multi-party computation has high communication overhead, slow reasoning and poor scalability for large-scale graphs, homomorphic encryption incurs