Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution Environment

Linkai Zhu, Shanwen Hu (), Xiaolian Zhu, Changpu Meng and Maoyi Huang
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Linkai Zhu: Information Technology School, Hebei University of Economics and Business, Shijiazhuang 050061, China
Shanwen Hu: Faculty of Data Science, City University of Macau, Macau 999078, China
Xiaolian Zhu: Information Technology School, Hebei University of Economics and Business, Shijiazhuang 050061, China
Changpu Meng: iFLYTEK Co., Ltd., Hefei 230088, China
Maoyi Huang: Product Development, Ericsson, 41756 Gothenburg, Sweden

Mathematics, 2023, vol. 11, issue 17, 1-19

Abstract: 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.

Keywords: federated learning; Trusted Execution Environment (TEE); blockchain; supply chain; Internet of Things (IoT) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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