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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 12
3527-3534

Survey on security and privacy protection in different scenarios of federated learning

Sun Shuang
Li Xiaohui
Liu Yan
Zhang Xing
School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121000, China
DOI: 10.19734/j.issn.1001-3695.2021.03.0157

Abstract

With the continuous development of big data, federated learning has been widely used in various scenarios to facilitate people's production and life. However, while this technology brings convenience to people, it also exposes users to the challenge of data leakage. Therefore, data security has become a hot issue in federated learning research. This paper introduced the training process of horizontal and vertical federated learning, and studied potential opponents of these processes and reasons for their attacks, so as to classify and summarize the existing attack methods, such as poisoning attacks, confrontation attacks, and model reverse attacks. Then, it introduced the defense measures against several attack methods in two scenarios, such as gradient sparseness, malicious detection, secret sample alignment, label protection, encryption sharing, and distur-bance sharing. Therefore, these methods not only could ensure the data security of the participants, but also could guarantee the accuracy of the joint model. On this basis, according to the research on the existing technology, it summarized the problems of the existing methods and the future research directions.

Foundation Support

国家自然科学基金青年基金资助项目(61802161)
辽宁省教育厅科学研究经费资助项目(JZL202015402,JZL202015404)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.03.0157
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 12
Section: Survey
Pages: 3527-3534
Serial Number: 1001-3695(2021)12-002-3527-08

Publish History

[2021-12-05] Printed Article

Cite This Article

孙爽, 李晓会, 刘妍, 等. 不同场景的联邦学习安全与隐私保护研究综述 [J]. 计算机应用研究, 2021, 38 (12): 3527-3534. (Sun Shuang, Li Xiaohui, Liu Yan, et al. Survey on security and privacy protection in different scenarios of federated learning [J]. Application Research of Computers, 2021, 38 (12): 3527-3534. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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