Privacy preserving classification on local differential privacy in data centers

Abstract With the rise of cloud service providers and the continuous virtualization of data centers, data center networks are also developing rapidly. As data centers become more and more complex, the demand for security increases dramatically. This paper discusses the privacy inherent in data centers. However, there is no general solution to the privacy problem in data centers due to the device heterogeneity. In this paper, we proposed a local differential privacy-based classification algorithm for data centers. In data mining of data centers, the differential privacy protection mechanism is added to deal with Laplace noise of sensitive information in the pattern mining process. We designed a method to quantify the quality of privacy protection through strict mathematical proof. Experiments demonstrated that the differential privacy-based classification algorithm proposed in this paper has higher iteration efficiency, better security and feasible accuracy. On the premise of ensuring availability, the algorithm has reliable privacy protection characteristics and excellent timeliness.