Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks

Theodoros Tsiolakis, Vasilis Perifanis, Nikolaos Pavlidis +3

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 empi…