Membership inference tests can be driven below random accuracy by poisoning the training set, even under relaxed, neighborhood-based membership definitions.
Transparency and accountability in ai systems: safeguarding wellbeing in the age of algorithmic decision-making.Frontiers in Human Dynamics, 6:1421273, 2024
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What Really is a Member? Discrediting Membership Inference via Poisoning
Membership inference tests can be driven below random accuracy by poisoning the training set, even under relaxed, neighborhood-based membership definitions.