Robust Source Anonymous Federated Learning Shuffle Protocol in IoT

Chen Jingxue; Gao Kehan; Zhou Erqiang; Qin Zhen

doi:10.7544/issn1000-1239.202330393

Journal of Computer Research and Development > 2023 > 60(10): 2218-2233. > DOI: 10.7544/issn1000-1239.202330393 CSTR: 32373.14.issn1000-1239.202330393

Chen Jingxue, Gao Kehan, Zhou Erqiang, Qin Zhen. Robust Source Anonymous Federated Learning Shuffle Protocol in IoT[J]. Journal of Computer Research and Development, 2023, 60(10): 2218-2233. DOI: 10.7544/issn1000-1239.202330393

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Robust Source Anonymous Federated Learning Shuffle Protocol in IoT

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054
Network and Data Security Key Laboratory of Sichuan Province (University of Electronic Science and Technology of China), Chengdu 610054

Funds: This Work was supported by the national key Research and Development Program of China (2022YFB2701400).

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Author Bio:
Chen Jingxue: born in 1996. PhD candidate. Her main research interests include IoT data collection and privacy-preserving computation

Gao Kehan: born in 1999. Master candidate. His main research interests include federated learning and privacy-preserving computation

Zhou Erqiang: born in 1980. PhD, associate professor. His main research interests include password security and natural language processing

Qin Zhen: born in 1983. PhD, professor, PhD supervisor. His main research interests include data fusion analysis, mobile social networks, wireless sensor networks, and image processing
Received Date: June 04, 2023
Revised Date: August 17, 2023
Available Online: October 07, 2023

Graphical Abstract

Abstract

Abstract

With the rapid development of Internet of things (IoT) and artificial intelligence (AI) technology, a large amount of data are collected by IoT devices. These data can be trained by using AI techniques such as machine learning or deep learning. A well-trained model is an important part of analyzing network environment and improving quality of service (QoS) in IoT. However, most data providers (IoT end users) are reluctant to share personal data directly with any third party for academic research or business analysis because personal data contains private or sensitive information. Therefore, it is an important research direction to study the security and privacy protection in the IoT. Federated learning (FL) allows different participants to keep their data locally and only upload the local training models to the parameter server for model aggregation, which protects the data privacy of each participant. However, FL still faces some security challenges. Concretely, there are two main attacks FL faces, i.e., inference attack and poisoning attack. In order to resist inference attacks and detect poisoning attacks simultaneously, we propose a source anonymous data shuffle scheme, Re-Shuffle. The proposed Re-Shuffle uses the oblivious transfer protocol to realize the anonymous upload of participant models in FL. It ensures that in the process of poisoning attack detection, the parameter server can obtain the local model of the participant, who is unknown. In addition, to be more suitable for the IoT environment, Re-Shuffle adopts a secret sharing mechanism, which ensures the rawness of gradient data and solves the problem of participants dropline in the traditional shuffle protocol. In this way, both the rawness and privacy of the local model are ensured, so that the poisoning attacks can be checked while the privacy is protected. Finally, we provide the security proof and evaluate the scheme’s detection effect. Besides, the computation overheads of Re-Shuffle under two kinds of poisoning attack detection schemes are evaluated. The results show that Re-Shuffle can provide privacy protection for the poisoning attacks detection scheme at an acceptable cost.
- Internet of things,
- privacy-preservation,
- federated learning,
- shuffle,
- poisoning attack detection

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References (39)

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