12 citations · 12 across the 5 of their papers we have counts for
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cs.CR2026
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
Jie Fu, Nima Naderloui, Da Zhong +2
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 s…
cs.CR2025
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…
cs.CR2024★ 12 cited
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling +6
In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de…