Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 18; H. 6; S. 4049 - 4058
Hauptverfasser: Jia, Bin, Zhang, Xiaosong, Liu, Jiewen, Zhang, Yang, Huang, Ke, Liang, Yongquan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Agglomeration
Blockchain
Cluster analysis
Clustering
Computational modeling
Cryptography
Data integrity
Data models
Data retrieval
differential privacy
Distributed databases
Federated learning
Fluctuations
homomorphic encryption
Industrial applications
Industrial Internet of Things
Information sharing
Machine learning
Privacy
privacy protection
Security
Vector quantization
Voltage fluctuations
ISSN:1551-3203, 1941-0050
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Zusammenfassung:With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3085960