A Privacy Protection Application of Consumer Personal Information Based on an Improved K-Anonymity Algorithm

In view of the limitations of the traditional Data mining algorithm in consumer personal information privacy protection application, a research scheme of personal information privacy protection application based on K-Anonymous algorithm was proposed. Firstly, the influencing factors is accurately located through Differential privacy dispensing theories, and the indicators is reasonably divided to reduce interference, and the K-Anonymous algorithm is used to construct a research scheme for personal information privacy protection application. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Data mining algorithm in terms of research accuracy and processing time of influencing factors of personal information privacy protection application, and has obvious advantages. The research of personal information privacy protection application plays an extremely important role in customer, which can accurately predict and optimize the growth characteristics and product generation of customer. However, the traditional Data mining algorithm has some limitations in solving the problem of information privacy protection simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of personal information privacy protection application based on K-Anonymous algorithm to better solve this problem. The scheme accurately locates the influencing factors through Differential privacy dispensing theories, so as to determine the division of indicators, and uses the K-Anonymous algorithm to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on K-Anonymous algorithm in consumer personal information privacy protection application can better solve the limitations of traditional Data mining algorithm and improve the simulation accuracy and efficiency.