4 papers
Rethinking Backdoor Repair Evaluation: Distinguishing Aggregate Clean Utility from Benign Performance Preservation
Baogang Song, Changtian Song, Jian Chen +4
Backdoor repair aims to suppress malicious behavior in compromised models while preserving benign task performance. Existing studies typically evaluate these objectives using Attac…
Keep Evaluation Fair: Detecting Data Leakage in Code Generation Benchmarks via Membership Inference Attacks
Dongdong Zhao, Jian Chen, Guancheng Lin +3
Code generation benchmarks are widely used to evaluate Large Language Models (LLMs), but benchmark data leakage into training sets can inflate performance and undermine evaluation…
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…
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…