Files and links (1)
CC BY V4.0, Open Access
Journal article
Secure and Efficient Smart Healthcare System Based on Federated Learning
International journal of intelligent systems, Vol.2023, 8017489
2023
Abstract
The rapid development of smart healthcare system in the Internet of Things (IoT) has made the early detection of many chronic diseases more convenient, quick, and economical. However, when healthcare organizations collect users' health data through deployed IoT devices, there are issues of compromising users' privacy. In view of this situation, this paper introduces federated learning technology to solve the problem of data security. In this paper, we consider the two main problems of federated learning applications in IoT smart healthcare system: (1) how to reduce the time overhead of system running and (2) how to authenticate that the user device uploading data is deployed by the system itself. To solve the above problems, we propose the first federated learning scheme based on full dynamic secret sharing. First, we use a two-mask protocol to keep the user's local model parameters confidential during federated learning. Then, based on homogeneous linear recursive equation, homomorphic hash function, and elliptic curve cryptosystem, the full dynamic secret sharing and user identity authentication are realized. In addition, our scheme allows users to join or quit during training. Finally, we have carried out simulation test on this scheme. The experimental results show that the efficiency of our scheme is improved by about 60% on average in the case of no user dropping and by about 30% in the case of some users dropping.
Details
- Title
- Secure and Efficient Smart Healthcare System Based on Federated Learning
- Authors/Creators
- Wei Liu - Xi’an University of Posts and TelecommunicationsYinghui Zhang - Xi’an University of Posts and TelecommunicationsGang Han - Xi’an University of Posts and TelecommunicationsJin Cao - Xidian UniversityHui Cui - Murdoch Univ, Perth, AustraliaDong Zheng - Xi’an University of Posts and Telecommunications
- Publication Details
- International journal of intelligent systems, Vol.2023, 8017489
- Publisher
- Wiley-Hindawi
- Number of pages
- 12
- Grant note
- 2021ZDLGY06-02; 2020ZDLGY08-04 / Key Research and Development Program of Shaanxi CXJJZL2021025; CXJJYL2021075 / Postgraduate Innovation Fund Program of Xian University of Posts and Telecommunications 62072369; 62072371 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) Shaanxi Special Support Program Youth Top-Notch Talent Program 2020KJXX-052 / Youth Innovation Team of Shaanxi Universities, Innovation Capability Support Program of Shaanxi
- Identifiers
- 991005617468207891
- Copyright
- © 2023 Wei Liu et al.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
Metrics
3 File views/ downloads
51 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.187 Security Systems
- 4.187.160 Cryptographic Protocols
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Engineering