FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things

【Author】 Yin, Bo; Yin, Hao; Wu, Yulei; Jiang, Zexun

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】With the explosive network data due to the advanced development of the Internet of Things (IoT), the demand for multiparty computation is increasing. In addition, with the advent of future digital society, data have been gradually evolving into an effective virtual asset for sharing and usage. With the nature of the sensitivity, massiveness, fragmentation, and security of multiparty data computation in the IoT environment, we propose a secure data collaboration framework (FDC) based on federated deep-learning technology. The proposed framework can realize the secure collaboration of multiparty data computation on the premise that the data do not need to be transmitted out of their private data center. This framework is empowered by public data center, private data center, and the blockchain technology. The private data center is responsible for data governance, data registration, and data management. The public data center is used for multiparty secure computation. The blockchain paradigm is responsible for ensuring secure data usage and transmissions. A real IoT scenario is used to validate the effectiveness of the proposed framework.

【Keywords】Data centers; Internet of Things; Data privacy; Collaboration; Security; Cyberspace; Data collaboration; data privacy; deep learning; federated learning; internet of Things (IoT)

【发表时间】2020 JUL

【收录时间】2022-01-02

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