UMIA

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set

arXiv:2605.01129v2 Announce Type: replace Abstract: Machine unlearning (MU) has emerged as a key mechanism for ensuring data privacy and regulatory compliance by enabling models to forget specific training samples. However, recent studies have shown that the removal of data can inadvertently introduce privacy leakages to the retain set,i.e., data that remain in the model after unlearning. In this paper, we extend the scope of privacy analysis in unlearning to the often-overlooked retained data.