Approximate unlearning leaves loss-landscape residuals that a fine-tuning-based attack can exploit for membership inference, and the proposed OUR method scrubs these residuals.
Membership inference at- tacks and defenses in federated learning: A survey
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Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Approximate unlearning leaves loss-landscape residuals that a fine-tuning-based attack can exploit for membership inference, and the proposed OUR method scrubs these residuals.