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arXiv Machine Learning By Joonas J\"alk\"o, Gauri Pradhan, Ossi R\"ais\"a, Antti Honkela

On Reliability of Membership Inference Vulnerability Evaluation

Read the original on arXiv Machine Learning →

The paper examines the reliability of membership inference attack (MIA) vulnerability evaluation. It identifies two weaknesses: finite‑sample bias from sampling shadow datasets from a fixed superset, and miscalibration when aggregating true positive rates across individuals at very low false positive rates. The authors propose simple fixes that avoid extra computational cost and suggest further improvements with additional computation.

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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