计算机科学
信任管理(信息系统)
块链
计算机安全
背景(考古学)
领域(数学分析)
领域(数学)
过程(计算)
协议(科学)
任务(项目管理)
工业互联网
激励
信息泄露
物联网
系统工程
医学
数学分析
古生物学
替代医学
数学
病理
纯数学
工程类
经济
生物
微观经济学
操作系统
作者
Xu Wu,Yang Liu,Jing Tian,Yuanpeng Li
标识
DOI:10.1016/j.knosys.2023.111166
摘要
The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.
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