Practical Bayes-Optimal Membership Inference Attacks

• URL: http://arxiv.org/abs/2505.24089v1
• Date: Fri, 30 May 2025 00:23:01 GMT
• Title: Practical Bayes-Optimal Membership Inference Attacks
• Authors: Marcus Lassila, Johan Östman, Khac-Hoang Ngo, Alexandre Graell i Amat,
• Abstract summary: We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data.<n>Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership inference rule for node-level MIAs against graph neural networks.
• Score: 57.06788930775812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Sablayrolles 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, computationally efficient approximations of the Bayes-optimal attack. 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.

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