Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0

• Published in: IEEE transactions on industrial informatics 2022-12, Vol.18 (12), p.8755-8764
• Main Authors: Yang, Fan, Qiao, Yanan, Abedin, Mohammad Zoynul, Huang, Cheng
• Format: Article
• Language: English
• Subjects: Blockchain; Blockchain technology; Blockchains; Collaborative work; Computational modeling; credit data sharing; Cryptography; Data models; Data retrieval; Data storage; Federated learning; Fourth Industrial Revolution; Industry 4.0; Privacy; privacy preserving; Security; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3151917

In this article, we aim to design an architecture for privacy-preserved credit data and model sharing to guarantee the secure storage and sharing of credit information in a distributed environment. The proposed architecture optimizes the data privacy by sharing the data model instead of revealing the actual data. This article also proposes an efficient credit data storage mechanism combined with a deletable Bloom filter to guarantee a uniform consensus for the training and computation process. In addition, we propose authority control contract and credit verification contract for the secure certification of credit sharing model results under federated learning. Extensive experimental results and security analysis demonstrate that our proposed credit model sharing system based on federated learning and blockchain is of high accuracy, efficiency, as well as stability. In particular, the findings of this article could alleviate the potential credit crisis under financial pressure that assist to economic recovery after the global COVID-19 pandemic. Our approach has further boosted up the demand for efficient, secure credit models for Industry 4.0.