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arXiv Machine Learning By Mahdi Haghifam, Adam Smith, Jonathan Ullman

The Sample Complexity of Membership Inference and Privacy Auditing

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The paper investigates the sample complexity required for membership‑inference attacks, focusing on Gaussian mean estimation. It shows that, in this setting, an attacker may need at least Ω(n + n^2 ho^2) reference samples to match a fully informed adversary, which can be far more than the n samples used by the learning algorithm. This demonstrates that practical attacks, which typically use only O(n) samples, might underestimate the true risk of membership inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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