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cs.CR2026
Discard the Dross and Select the Essential: Pre-query Sample Selection for Black-box Membership Inference Attacks
Dongdong Zhao, Jinrong Hu, Changtian Song +3
Black-box membership inference attacks (MIAs) rely on target-model queries to infer whether candidate samples were used for training. However, membership signals are highly non-uni…
cs.CR2025
FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
Jian Chen, Zehui Lin, Wanyu Lin +3
Recently, the practical needs of ``the right to be forgotten'' in federated learning gave birth to a paradigm known as federated unlearning, which enables the server to forget pers…