Low-cohesion differential privacy protection for industrial Internet
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Low‑cohesion differential privacy protection for industrial Internet Jun Hou1 · Qianmu Li2,3,4 · Shicheng Cui2 · Shunmei Meng2 · Sainan Zhang2 · Zhen Ni3 · Ye Tian5
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Differential privacy protection · Industrial Internet · CPS
* Qianmu Li [email protected] 1
Department of Social Sciences, Nanjing Institute of Industry Technology, 210023, Nanjing, China
2
School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3
School of Information Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China
4
Jiangsu Zhongtian Internet Technology Co., Ltd., Jiangsu Zhongtian Technology Co., Ltd., Nantong 226463, China
5
China Information Communication Technologies Group Corporation, Wuhan 430000, China
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J. Hou et al.
1 Introduction Cyber physical systems (CPSs) greatly improve the degree of industrial intelligence and improve the efficiency of production links and supply chains, but they also provide an opportunity for lawless elements to obtain the relevant data [1–6] more easily, and then steal the privacy information such as production line control information, product logistics trajectory and product sales status [7, 8]. This will not only affect the normal operation of the industrial Internet system, infringe on the privacy of enterprises, but also may cause serious harm to the reputation, property and even safety in production of enterprises [9]. If these security and privacy threats are not effectively addressed, the further development of industrial Internet systems will be directly affected. Therefore, there is an urgent need for a comprehensive and systematic study of the security and privacy of industrial Internet systems [10–16]. At present, CPS such as the power industry Internet is becoming more intelligent and collecting more and more data, which is of positive significance for improving user experience or improving system performance. But it also provides an opportunity for criminals. It is easier for criminals to obtain relevant d
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