Open AccessResearch on the Security Technology of Federated Learning Privacy Preserving
Author(s) -
Juan Mao,
Chunjie Cao,
Longjuan Wang,
Jun Ye,
Wenjie Zhong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012192
Subject(s) - premise , computer science , computer security , internet privacy , information privacy , privacy by design , personally identifiable information , differential privacy , cloud computing security , privacy protection , privacy software , data sharing , cloud computing , data mining , medicine , philosophy , linguistics , alternative medicine , pathology , operating system
With the emergence of data islands and the popular awareness of privacy, federated learning, as an emerging data sharing and exchange model, can realize multi-party collaboration under the premise of protecting data privacy and security because the data distributed in multiple devices cannot be sent locally. To achieve benefits for all parties involved, it has been widely used in many fields such as finance, medical care, and education. However, FL also has various security and privacy issues. Starting from the overview of federated learning, this article describes in detail the threat model and existing security issues, including replay attacks, poisoning attacks, reasoning attacks, etc., and then makes a certain analysis of FL privacy protection security technologies. Compared with SMC and HE, differential privacy is excellent in terms of efficiency. Finally, we discussed the challenges of privacy protection and security issues and future research directions.