arXiv Machine Learning September 23, 2026 By Anum Fatima, Stratis Limnios, James Adams, Lukasz Szpruch, Carsten Maple, Gesine Reinert, Andrew Elliott

Empirical Auditing of Edge-Private Graph Generators

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The paper presents an empirical audit of privacy leakage in edge‑private graph generators by testing whether outputs from edge‑neighbouring inputs remain distinguishable. It introduces statistically valid lower bounds on privacy loss and compares direct‑edge, local‑structural, and GNN‑based attacks based on the geometry around a target edge. Experiments on two generators and two networks reveal that privacy leakage varies with both the mechanism and the network, and that learned representations expose information beyond conventional local statistics.

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