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 Type	Paper
Authors	Xie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di
Journal or Proceedings Title	Proceedings of 2021 17th International Conference on Mobility, Sensing and Networking (MSN)
Journal Citation	pp. 153-160
Number of Pages	8
Year	2021
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	United States
Digital Object Identifier (DOI)	https://doi.org/10.1109/MSN53354.2021.00036
Web Address (URL) of Paper	https://ieeexplore.ieee.org/abstract/document/9751527
Web Address (URL) of Conference Proceedings	https://ieeexplore.ieee.org/xpl/conhome/9751460/proceeding
Conference/Event	2021 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 Content	Does not contain sensitive content
ANZSRC Field of Research 2020	460407. System and network security
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	Nanjing University of Aeronautics and Astronautics, China
	Yangzhou University, China
	Deakin University

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