Secure and Private Graph Neural Networks for Social Network Analysis

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing social networks, enabling the extraction of meaningful patterns and insights from complex relational data. However, the application of GNNs in social network analysis raises significant concerns regarding data privacy and security, particularly when sensitive user information is involved. This paper explores the integration of privacy-preserving techniques within GNN frameworks, focusing on methods such as differential privacy, homomorphic encryption, and federated learning. We propose a novel GNN architecture that incorporates these techniques to ensure secure data handling while maintaining the model's performance. Our experimental results demonstrate that the proposed model achieves competitive accuracy in social network tasks while effectively safeguarding user privacy. This research contributes to the growing field of secure machine learning, highlighting the importance of privacy in the deployment of GNNs for social network analysis.