Survey on privacy protection in non-aggregated data sharing [面向隐私保护的非聚合式数据共享综述]
- Authors
- Li, Y.; Yin, Y.; Gao, H.; Jin, Y.; Wang, X.
- Issue Date
- Jun-2021
- Publisher
- Editorial Board of Journal on Communications
- Keywords
- Data sharing; Federated learning; Privacy protection; Secure multi-party computation
- Citation
- Tongxin Xuebao/Journal on Communications, v.42, no.6, pp.195 - 212
- Journal Title
- Tongxin Xuebao/Journal on Communications
- Volume
- 42
- Number
- 6
- Start Page
- 195
- End Page
- 212
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81686
- DOI
- 10.11959/j.issn.1000-436x.2021120
- ISSN
- 1000-436X
- Abstract
- Although there is a great value hidden in the massive data, it can also easily expose user privacy. Aiming at efficiently and securely sharing data from multiple parties and avoiding leakage of user private information, the development of related research and technologies on the non-aggregated data sharing field was introduced. Firstly, secure multi-party computing and its technologies were briefly described, including homomorphic encryption, oblivious transfer, secret sharing, etc. Secondly, the federated learning architecture was analyzed from the aspects of source data nodes and transmission optimization. Finally, the existing non-aggregated data sharing frameworks were listed and compared. In addition, the challenges and future potential research directions were summarized, such as complex multi-party scenarios, the balance between optimization and cost, as well as related security risks. © 2021, Editorial Board of Journal on Communications. All right reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81686)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.