7 citations · 11 across the 19 of their papers we have counts for
4 papers · 1 filter
UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
Lulu Xue, Shengshan Hu, Wei Lu +6
Machine unlearning is an emerging technique that aims to remove the influence of specific data from trained models, thereby enhancing privacy protection. However, recent research h…
MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Yuxuan Ning, Zhicong Huang +7
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this…
DarkFed: A Data-Free Backdoor Attack in Federated Learning
Minghui Li, Wei Wan, Yuxuan Ning +4
Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real cli…
Evaluating Membership Inference Through Adversarial Robustness
Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng +2
The usage of deep learning is being escalated in many applications. Due to its outstanding performance, it is being used in a variety of security and privacy-sensitive areas in add…