Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution Environment Article Swipe
Linkai Zhu
,
Shanwen Hu
,
Xiaolian Zhu
,
Changpu Meng
,
Maoyi Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11173759
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11173759
Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11173759
- https://www.mdpi.com/2227-7390/11/17/3759/pdf?version=1693549975
- OA Status
- gold
- Cited By
- 6
- References
- 41
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.3390/math11173759Digital Object Identifier
- Title
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Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution EnvironmentWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-01Full publication date if available
- Authors
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Linkai Zhu, Shanwen Hu, Xiaolian Zhu, Changpu Meng, Maoyi HuangList of authors in order
- Landing page
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https://doi.org/10.3390/math11173759Publisher landing page
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https://www.mdpi.com/2227-7390/11/17/3759/pdf?version=1693549975Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/17/3759/pdf?version=1693549975Direct OA link when available
- Concepts
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Blockchain, Upload, Computer science, Computer security, Scheme (mathematics), Supply chain, Process (computing), Operating system, Mathematical analysis, Mathematics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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