【Author】
Lu, Yunlong; Huang, Xiaohong; Dai, Yueyue; Maharjan, Sabita; Zhang, Yan
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.
【Keywords】Distributed databases; Blockchain; Data models; Data privacy; Machine learning; Collaboration; Security; Data sharing; federated learning; industrial Internet of Things (IIoT); permissioned blockchain; privacy-preserved
【标题】工业物联网中隐私保护数据共享的区块链和联邦学习
【摘要】工业物联网范式中连接设备产生的数据量的快速增加，为通过数据共享提高新兴应用的服务质量开辟了新的可能性。然而，安全和隐私问题（例如，数据泄露）是数据提供商在无线网络中共享数据的主要障碍。私人数据的泄漏可能会导致供应商遭受经济损失以外的严重问题。在本文中，我们首先为分布式多方设计了一个区块链赋能的安全数据共享架构。然后，我们通过结合隐私保护联邦学习将数据共享问题转化为机器学习问题。通过共享数据模型而不是透露实际数据，数据的隐私得到了很好的维护。最后，我们将联邦学习整合到许可区块链的共识过程中，使得共识的计算工作也可以用于联邦训练。从实际数据集得出的数值结果表明，所提出的数据共享方案具有良好的准确性、高效率和增强的安全性。
【关键词】分布式数据库；区块链；数据模型；数据隐私；机器学习；合作;安全;数据共享；联邦学习；工业物联网（IIoT）；许可的区块链；隐私保护
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