2026
Revisiting Asymmetries in Black-box Link Stealing against Graph Neural Networks
ICML 2026poster
Graph Neural Networks (GNNs) are increasingly deployed on sensitive relational data, from social networks to healthcare records. However, their outputs can leak private graph structure, enabling link-stealing attacks that infer whether a connection between two entities existed in the training graph.…