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

【Author】 Yang, Fan; Qiao, Yanan; Abedin, Mohammad Zoynul; Huang, Cheng

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】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.

【Keywords】Data models; Blockchains; Collaborative work; Training; Security; Fourth Industrial Revolution; Computational modeling; Blockchain technology; credit data sharing; federated learning; Industry 4; 0; privacy preserving

【发表时间】2022 DEC

【收录时间】2022-10-17

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习