Poisoning attack in federated learning using generative adversarial nets

Paper

Zhang, Jiale, Chen, Junjun, Wu, Di, Chen, Bing and Yu, Shui. 2019. "Poisoning attack in federated learning using generative adversarial nets." 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). Rotorua, New Zealand 05 - 08 Aug 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/TrustCom/BigDataSE.2019.00057
Paper/Presentation Title

Poisoning attack in federated learning using generative adversarial nets

Presentation TypePaper
AuthorsZhang, Jiale, Chen, Junjun, Wu, Di, Chen, Bing and Yu, Shui
Journal or Proceedings TitleProceedings of 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Journal Citationpp. 374-380
Number of Pages7
Year2019
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/TrustCom/BigDataSE.2019.00057
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/8887357
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8883860/proceeding
Conference/Event2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Event Details
2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Parent
IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
Delivery
In person
Event Date
05 to end of 08 Aug 2018
Event Location
Rotorua, New Zealand
Abstract

Federated learning is a novel distributed learning framework, where the deep learning model is trained in a collaborative manner among thousands of participants. The shares between server and participants are only model parameters, which prevent the server from direct access to the private training data. However, we notice that the federated learning architecture is vulnerable to an active attack from insider participants, called poisoning attack, where the attacker can act as a benign participant in federated learning to upload the poisoned update to the server so that he can easily affect the performance of the global model. In this work, we study and evaluate a poisoning attack in federated learning system based on generative adversarial nets (GAN). That is, an attacker first acts as a benign participant and stealthily trains a GAN to mimic prototypical samples of the other participants' training set which does not belong to the attacker. Then these generated samples will be fully controlled by the attacker to generate the poisoning updates, and the global model will be compromised by the attacker with uploading the scaled poisoning updates to the server. In our evaluation, we show that the attacker in our construction can successfully generate samples of other benign participants using GAN and the global model performs more than 80% accuracy on both poisoning tasks and main tasks.

KeywordsFederated learning; poisoning attack; generative adversarial nets; security; privacy
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
4604. Cybersecurity and privacy
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Byline AffiliationsNanjing University of Aeronautics and Astronautics, China
Beijing University of Chemical Technology, China
University of Technology Sydney
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