GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

Bang Wu

Network and Distributed System Security (NDSS) Symposium 2024 · Day 3 · ML Security & Privacy

Graph Neural Networks (GNNs) have emerged as a transformative technology for analyzing complex graph-structured data across diverse fields, from e-Commerce recommendations to advanced drug discovery and protein folding. With the increasing adoption of Machine Learning as a Service (MLaaS) platforms, GNN models are frequently deployed in cloud environments, offering public APIs for predictions. While convenient, this paradigm introduces significant transparency challenges, particularly regarding the local training processes undertaken by model developers. This opacity creates a critical vulnerability: the potential for unauthorized accumulation and misuse of vast amounts of graph data, directly infringing upon the intellectual property (IP) rights of data owners.