arXiv:2601.17130v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely used for tasks such as node classification and link prediction, but their use in sensitive settings raises concerns about training-data leakage. Prior work on privacy leakage in GNNs largely borrows assumptions from non-graph domains, overlooking the role of graph structure. We argue for a graph-specific analysis of privacy risk and study how graph structure affects node-level membership inference. We formalize membership inference (MI) over node-neighborhood tuples and investigate two important dimensi
The proliferation of GNNs in sensitive areas and the increasing awareness of data privacy vulnerabilities make this privacy research critical and timely.
This research provides a foundational understanding of privacy risks specific to graph-structured data, which is essential for secure AI deployment in critical systems.
The understanding of Graph Neural Network (GNN) privacy risks shifts from generalized assumptions to graph-specific analyses, influencing future secure GNN design and deployment.
- · AI security researchers
- · Organizations handling sensitive graph data
- · Companies developing privacy-preserving AI
- · Developers ignoring graph-specific privacy risks
- · Bad actors exploiting data leakage from GNNs
Increased focus on designing privacy-preserving GNN architectures and training methodologies.
New regulatory guidelines or industry standards for GNN deployment, particularly in sensitive sectors like healthcare or finance.
The development of a specialized sub-field within AI security focused solely on graph privacy and its unique challenges.
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Read at arXiv cs.LG