FedInverse: Evaluating Privacy Leakage in Federated Learning

Paper


Wu, Di, Bai, Jun, Song,Yiliao, Chen, Junjun, Zhou, Wei, Xiang, Yong and Sajjanhar, Atul. 2024. "FedInverse: Evaluating Privacy Leakage in Federated Learning." The Twelfth International Conference on Learning Representations. Vienna, Austria 07 - 11 May 2024 Austria.
Paper/Presentation Title

FedInverse: Evaluating Privacy Leakage in Federated Learning

Presentation TypePaper
AuthorsWu, Di, Bai, Jun, Song,Yiliao, Chen, Junjun, Zhou, Wei, Xiang, Yong and Sajjanhar, Atul
Journal or Proceedings TitleThe Twelfth International Conference on Learning Representations
Number of Pages31
Year2024
Place of PublicationAustria
Web Address (URL) of Paperhttps://openreview.net/forum?id=nTNgkEIfeb
Web Address (URL) of Conference Proceedingshttps://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral
Conference/EventThe Twelfth International Conference on Learning Representations
Event Details
The Twelfth International Conference on Learning Representations
Delivery
In person
Event Date
07 to end of 11 May 2024
Event Location
Vienna, Austria
Event Venue
Messe Wien Exhibition and Congress Center
Event Web Address (URL)
Abstract

Federated Learning (FL) is a distributed machine learning technique where multiple devices (such as smartphones or IoT devices) train a shared global model by using their local data. FL promises better data privacy as the individual data isn’t shared with servers or other participants. However, this research uncovers a groundbreaking insight: a model inversion (MI) attacker, who acts as a benign participant, can invert the shared global model and obtain the data belonging to other participants. In such scenarios, distinguishing between attackers and benign participants becomes challenging, leading to severe data-leakage risk in FL. In addition, we found even the most advanced defense approaches could not effectively address this issue. Therefore, it is important to evaluate such data-leakage risks of an FL system before using it. Motivated by that, we propose FedInverse to evaluate whether the FL global model can be inverted by MI attackers. In particular, FedInverse can be optimized by leveraging the Hilbert-Schmidt independence criterion (HSIC) as a regularizer to adjust the diversity of the MI attack generator. We test FedInverse with three typical MI attackers, GMI, KED-MI, and VMI. The experiments show that FedInverse can effectively evaluate the data leakage risk that attackers successfully obtain the data belonging to other participants. The code of this work is available at https://github.com/Jun-B0518/FedInverse

Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
4604. Cybersecurity and privacy
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Byline AffiliationsSchool of Mathematics, Physics and Computing
Deakin University
University of Adelaide
Peking University, China
Swinburne University of Technology
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