Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. Existing MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge distillation. Unfortunately, using either of...

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Bibliographic Details
Main Authors: Jun Niu, Peng Liu, Chunhui Huang, Yangming Zhang, Moxuan Zeng, Kuo Shen, Yangzhong Wang, Suyu An, Yulong Shen, Xiaohong Jiang, Jianfeng Ma, He Wang, Gaofei Wu, Anmin Fu, Chunjie Cao, Xiaoyan Zhu, Yuqing Zhang
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-01-01
Series:Journal of Information and Intelligence
Subjects:
Machine learning
Membership inference defenses
Preemptive exclusion
Knowledge distillation
Online Access:http://www.sciencedirect.com/science/article/pii/S2949715924000556
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