Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks
Membership Inference Attacks (MIAs) pose a significant privacy risk by enabling adversaries to determine if a specific data point was part of a model's training set. This work empirically investigates whether MU algorithms can function as a targeted, active defense mechanism, in scenarios where a privacy audit identifies specific classes or individuals as highly susceptible to MIAs post-training. By 'dulling' the model's categorical memory of these samples, the process effectively mitigates the membership signal and reduces the MIA success rate for the most vulnerable users. We evaluate the defense potential of three MU algorithms, Negative Gradient (neg grad), SCalable Remembering and Unlearning unBound (SCRUB), and Selective Fine-tuning and Targeted Confusion (SFTC), across four diverse datasets and three complexity-based model groups. Our findings reveal that MU can function as a countermeasure against MIAs, though its success is critically contingent on algorithm choice, model capacity, and a profound sensitivity to learning rates. While Negative Gradient often induces a generalized degradation of membership signals across both forget and retain set, SFTC identifies a critical ``divergence effect'' where targeted forgetting reinforces the membership signal of retained data. Conversely, SCRUB provides a more balanced defense with minimal collateral impact on MIA perspective.