Defending against membership inference attacks in federated learning via adversarial example

Paper


Xie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di. 2021. "Defending against membership inference attacks in federated learning via adversarial example." 2021 17th International Conference on Mobility, Sensing and Networking (MSN). Exeter, United Kingdom 13 - 15 Dec 2021 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/MSN53354.2021.00036
Paper/Presentation Title

Defending against membership inference attacks in federated learning via adversarial example

Presentation TypePaper
AuthorsXie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di
Journal or Proceedings TitleProceedings of 2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Journal Citationpp. 153-160
Number of Pages8
Year2021
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/MSN53354.2021.00036
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/9751527
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9751460/proceeding
Conference/Event2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Event Details
2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Parent
International Conference on Mobility, Sensing and Networking
Delivery
In person
Event Date
13 to end of 15 Dec 2021
Event Location
Exeter, United Kingdom
Abstract

Federated learning has attracted attention in recent years due to its native privacy-preserving features. However, it is still vulnerable to various membership inference attacks, such as backdoor, poisoning, and adversarial attacks. Membership Inference attack aims to discover the data used to train the model, which leads to privacy leaking ramifications on participants who use their local data to train the shared model. Recent research on countermeasure methods mainly focuses on protecting the parameters and has limitations in guaranteeing privacy while restraining the loss of the model. This paper proposes Fedefend, which applies adversarial examples to defend against membership inference attacks in federated learning. The proposed approach adds well-designed noise to the attack features of the target model of each iteration becomes an adversarial example. In addition, we also consider the utility loss of the model and use an adversarial method to generate noise to constrain the loss to a certain extent, which efficiently achieves a trade-off between privacy security and loss of the federated learning model. We evaluate the proposed Fedefend on two benchmark datasets, and the experimental results demonstrate that Fedefend has a good performance.

Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460407. System and network security
Public Notes

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Byline AffiliationsNanjing University of Aeronautics and Astronautics, China
Yangzhou University, China
Deakin University
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