【Author】
Yin, Bo; Yin, Hao; Wu, Yulei; Jiang, Zexun
【Source】IEEE INTERNET OF THINGS JOURNAL
【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)
【标题】FDC：用于物联网数据协作的安全联合深度学习机制
【摘要】随着物联网（IoT）的先进发展，网络数据的爆炸式增长，对多方计算的需求不断增加。此外，随着未来数字社会的到来，数据逐渐演变为一种有效的共享和使用虚拟资产。鉴于物联网环境中多方数据计算的敏感性、海量、碎片化和安全性，我们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）。所提出的框架可以在数据不需要从他们的私有数据中心传输出去的前提下，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赋能。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。区块链范式负责确保安全的数据使用和传输。一个真实的物联网场景用于验证所提出框架的有效性。
【关键词】数据中心；物联网;数据隐私；合作;安全;网络空间；数据协作；数据隐私；深度学习；联邦学习；物联网 (IoT)
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