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"Enhancing the Security and Privacy in the IoT Supply Chain Using Block" by Linkai Zhu, Shanwen Hu et al.

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 ....

Intel Software Guard Extensions Sgxs , Trusted Execution Environment , Intel Software Guard Extensions , Federated Learning , Internet Of Things Iot , Supply Chain , Rusted Execution Environment Tee ,