FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
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DOI：
10.1109/jiot.2020.2966778
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发表时间：
2020-01
影响因子：
10.6
通讯作者：
Bo Yin;Hao Yin;Yulei Wu;Zexun Jiang
中科院分区：
文献类型：
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作者：
Bo Yin;Hao Yin;Yulei Wu;Zexun Jiang
随着物联网（IoT）的发展，网络数据爆炸式增长，对多方计算的需求日益增加。此外，随着未来数字社会的到来，数据已经逐渐演变成一种有效的虚拟资产，可供共享和使用。针对物联网环境下多方数据计算的敏感性、海量性、碎片性和安全性等特点，提出了一种基于联邦深度学习技术的安全数据协作框架（FDC）。该框架可以在不需要将数据传输出其私有数据中心的前提下，实现多方数据计算的安全协作。该框架由公共数据中心、私有数据中心和区块链技术提供支持。私有数据中心负责数据治理、数据注册和数据管理。公共数据中心用于多方安全计算。区块链范例负责确保安全的数据使用和传输。一个真实的物联网场景被用来验证所提出框架的有效性。
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.