Differential Privacy for Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and learning from graph-structured data, finding applications in diverse domains such as social network analysis, drug discovery, and recommendation systems. However, the training of GNNs presents unique privacy challenges due to the intricate relationships between nodes within a graph. Directly applying standard differential privacy techniques to GNNs can lead to significant information leakage, effectively disrupting the learned graph structure and potentially compromising node relationships. This work introduces a novel approach to differentially private GNN training that aims to mitigate these challenges. Our methodology focuses on preserving both the overall graph connectivity and the specific relationships between nodes while simultaneously providing robust privacy guarantees. We formulate the problem as a constrained optimization, considering both the privacy loss and the graph reconstruction error. The key innovation lies in a carefully designed aggregation mechanism that incorporates noise in a way that minimizes the impact on graph structure. The theoretical analysis demonstrates the feasibility of achieving a desired privacy level while maintaining a reasonable level of accuracy. Experimental results, though limited to synthetic datasets for this initial exploration, suggest the potential of our approach.