Due to the increasing intelligence of data acquisition and analysis in cyber physical systems (CPSs) and the emergence of various transmission vulnerabilities, this paper proposes a differential privacy protection method for frequent pattern mining in view of the application-level privacy protection requirements of industrial interconnected systems. This method designs a low-cohesion algorithm to realize differential privacy protection. In the implementation of differential privacy protection, Top-k frequent mode method is introduced, which combines the factors of index mechanism and low cohesive weight of each mode, and the original support of each selected mode is disturbed by Laplacian noise. It achieves a balance between privacy protection and utility, guarantees the trust of all parties in CPS and provides an effective solution to the problem of privacy protection in industrial Internet systems.

中文翻译：

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工业互联网低内聚差分隐私保护

由于网络物理系统（CPS）中数据采集和分析的智能化程度不断提高，以及各种传输漏洞的出现，本文针对工业互联的应用级隐私保护需求，提出了一种频繁模式挖掘的差分隐私保护方法。系统。该方法设计了一种低内聚算法来实现差分隐私保护。在差分隐私保护的实现中，引入Top-k频繁模式方法，结合各模式的指标机制和低内聚权重等因素，各选择模式的原始支撑受到拉普拉斯噪声的干扰。它实现了隐私保护和实用性之间的平衡，