Efficient Membership Inference Attacks against Federated Learning via Bias Differences-Reference-Cited by-同舟云学术

Efficient Membership Inference Attacks against Federated Learning via Bias Differences

Published:2023-10-16 Issue: Volume: Page:
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Container-title:Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses
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Author:

Zhang Liwei¹^ORCID,Li Linghui¹^ORCID,Li Xiaoyong¹^ORCID,Cai Binsi¹^ORCID,Gao Yali¹^ORCID,Dou Ruobin²^ORCID,Chen Luying³^ORCID

Affiliation:

1. Key Laboratory of Trustworthy Distributed Computing and Service (MoE), Beijing University of Posts and Telecommunications, China

2. China Mobile Group Tianjin Co.,Itd., China

3. HAOHAN Data Technology Co.,ltd, China

Funder

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3607199.3607204

Reference48 articles.

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4. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization

5. Rishav Chourasia , Batnyam Enkhtaivan , Kunihiro Ito , Junki Mori , Isamu Teranishi , and Hikaru Tsuchida . 2021. Knowledge Cross-Distillation for Membership Privacy. arXiv preprint arXiv:2111.01363 ( 2021 ). Rishav Chourasia, Batnyam Enkhtaivan, Kunihiro Ito, Junki Mori, Isamu Teranishi, and Hikaru Tsuchida. 2021. Knowledge Cross-Distillation for Membership Privacy. arXiv preprint arXiv:2111.01363 (2021).

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