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

Fan Yang Orcid Logo, Yanan Qiao Orcid Logo, 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...

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Published in: IEEE Transactions on Industrial Informatics
ISSN: 1551-3203 1941-0050
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64232
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first_indexed 2023-09-20T14:09:30Z
last_indexed 2023-09-20T14:09:30Z
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spelling v2 64232 2023-08-31 Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article IEEE Transactions on Industrial Informatics 18 12 8755 8764 Institute of Electrical and Electronics Engineers (IEEE) 1551-3203 1941-0050 Blockchain technology, credit data sharing, federated learning, Industry 4.0, privacy preserving 31 12 2022 2022-12-31 10.1109/tii.2022.3151917 http://dx.doi.org/10.1109/tii.2022.3151917 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-20T15:09:31.6155463 2023-08-31T17:34:38.8403122 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Fan Yang 0000-0003-1842-1084 1 Yanan Qiao 0000-0002-5739-355x 2 Abedin Abedin 3 Cheng Huang 4
title Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
spellingShingle Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
Abedin Abedin
title_short Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
title_full Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
title_fullStr Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
title_full_unstemmed Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
title_sort Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Fan Yang
Yanan Qiao
Abedin Abedin
Cheng Huang
format Journal article
container_title IEEE Transactions on Industrial Informatics
container_volume 18
container_issue 12
container_start_page 8755
publishDate 2022
institution Swansea University
issn 1551-3203
1941-0050
doi_str_mv 10.1109/tii.2022.3151917
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1109/tii.2022.3151917
document_store_str 0
active_str 0
description 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.
published_date 2022-12-31T15:09:30Z
_version_ 1777565927437500416
score 11.016593

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