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 significant latency unable to meet real-time needs, and trusted execution environments depend on specific hardware with limited versatility. In contrast, differential privacy (DP) offers provable security guarantees, does not depend on auxiliary information, and avoids hardware dependencies, making it a more practical privacy-preserving technology for graph classification scenarios. To address the challenge of balancing privacy protection and classification accuracy in graph classification, this paper integrates adaptive differential privacy into Graph Convolutional Networks (DPANG-GCN) to propose a privacy-enhanced model. The core idea is to introduce gradient noise that satisfies DP constraints into the model training process and dynamically optimize the noise magnitude in each iteration. By adjusting the noise level according to the trend of loss changes, the update direction of model parameters is ensured to be conducive to minimizing the loss function, thereby alleviating performance degradation caused by excessive noise. We conducted experiments on four classic graph classification datasets, which demonstrated the effectiveness of our proposed method.
Song et al. (Fri,) studied this question.