Enhanced Privacy Protection in Graph Neural Networks Using Local Differential Privacy

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in processing graph data, showcasing immense potential across various applications. However, concerns arise regarding privacy leakage when GNNs are applied to graphs containing sensitive data. Existing privacy protection methods for GNNs often introduce excessive noise, leading to prolonged computation time. In this study, we propose a GNN privacy-enhancing algorithm based on local differential privacy. Specifically, we transmit data processed by the encoder and rectifier to a graph convolutional layer named MSMA. This graph convolutional layer utilizes multi-hop aggregation of node features as a denoising mechanism to further mitigate the impact of injected noise. Subsequently, the denoised data is forwarded to the GNN layer for processing, and its eligibility for early termination after validation testing is assessed. Additionally, we devise an early termination strategy that significantly reduces runtime with minimal impact on accuracy, thereby conserving computational resources. Extensive experiments conducted on real-world datasets demonstrate that our approach effectively mitigates privacy loss while maintaining satisfactory accuracy and computational efficiency.