Safeguarding Graph Neural Networks against Topology Inference Attacks

Graph Neural Networks (GNNs) have gained prominence for their ability to learn from graph-structured data, yet their adoption raises serious privacy concerns, particularly regarding topology privacy. A recent study reveals that GNNs are highly susceptible to topology inference attacks, which can reconstruct the overall structure of a target training graph with mere black-box access to the model. This vulnerability underscores the inadequacy of existing edge-level differential privacy mechanisms, which either fail to mitigate risks or compromise model accuracy. In response, researchers introduced Private Graph Reconstruction (PGR), a novel defense framework that addresses these issues. PGR is designed as a bi-level optimization problem, significantly reducing topology leakage while preserving model performance. This advancement is crucial as it not only enhances the security of GNNs but also encourages their responsible use in sensitive applications.