Empirical Auditing of Edge-Private Graph Generators

Abstract:We empirically audit privacy leakage by testing whether outputs from edge-neighbouring inputs remain distinguishable, using statistically valid lower bounds on the privacy loss witnessed by our attacks. Our framework compares direct-edge, local-structural, and GNN-based attacks through the geometry surrounding a target edge. Experiments across two generators and two networks show that privacy leakage is both mechanism- and network-dependent, with learned representations revealing information not captured by conventional local statistics.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2609.25155 [stat.ML]

(or arXiv:2609.25155v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.25155 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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