AuthorsY. Lu, X. Huang, Y. Dai, S. Maharjan and Y. Zhang
TitleBlockchain and Federated Learning for Privacy-preserved Data Sharing in Industrial IoT
AfilliationCommunication Systems
Project(s)Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications
StatusPublished
Publication TypeJournal Article
Year of Publication2019
Journal IEEE Transactions on Industrial Informatics (Early Access)
Publisher IEEE
Abstract

The rapid increase in the volume of data generated from connected devices in Industrial Internet of Things (IIoT) 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 propose a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we transfer the data sharing problem into a machine learning problem by incorporating privacy-preserved federated learning. The privacy of data is well maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain thus the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency and enhanced security.

Citation Key26979