Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
Beijing University of Posts and Telecommunications · University of Electronic Science and Technology of China · +2 more institutions
Abstract
The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated…
Citation impact
- FWCI
- 76.61
- Percentile
- 100%
- References
- 31
Authors
5- YLYunlong LuCorresponding
Beijing University of Posts and Telecommunications
- XHXiaohong Huang
Beijing University of Posts and Telecommunications
- YDYueyue Dai
University of Electronic Science and Technology of China
- SMSabita Maharjan
University of Oslo, Simula Metropolitan Center for Digital Engineering
- YZYan Zhang
University of Oslo
Topics & keywords
- Computer science
- Blockchain
- Data sharing
- Information privacy
- Data modeling
- Data security
- Big data
- Federated learning
- Industry, innovation and infrastructure