Journal article 434 views
Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
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Yanan Qiao ,
Abedin Abedin,
Cheng Huang
IEEE Transactions on Industrial Informatics, Volume: 18, Issue: 12, Pages: 8755 - 8764
Swansea University Author: Abedin Abedin
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DOI (Published version): 10.1109/tii.2022.3151917
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 th...
| Published in: | IEEE Transactions on Industrial Informatics |
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| ISSN: | 1551-3203 1941-0050 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64232 |
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| 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. |
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| Keywords: |
Blockchain technology, credit data sharing, federated learning, Industry 4.0, privacy preserving |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
12 |
| Start Page: |
8755 |
| End Page: |
8764 |