Practical Bayes-Optimal Membership Inference Attacks

We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i. i. d. ) data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sabrayolles et al. , we derive the Bayes-optimal membership inference rule for node-level MIAs against graph neural networks, addressing key open questions about optimal query strategies in the graph setting. We introduce BASE and G-BASE, tractable approximations of the Bayes-optimal membership inference. G-BASE achieves superior performance compared to previously proposed classifier-based node-level MIA attacks. BASE, which is also applicable to non-graph data, matches or exceeds the performance of prior state-of-the-art MIAs, such as LiRA and RMIA, at a significantly lower computational cost. Finally, we show that BASE and RMIA are equivalent under a specific hyperparameter setting, providing a principled, Bayes-optimal justification for the RMIA attack.

Authors

• Marcus Lassila
• Johan Oestman
• Khac-Hoang Ngo
• Alexandre Graell i Amat

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