Privacy protection clustering method for big data analysis and computer storage medium

Abstract

The invention discloses a privacy protection clustering method for big data analysis and a computer storage medium. The method comprises the following steps: normalizing data and selecting a central point; calculating a minimum privacy budget and distributing a privacy budget sequence, dividing a sample point to a nearest center point, generating Laplace noise, adding noise to parameters in the process of updating the center point, and performing continuously iterating until the difference of error quadratic sums of two adjacent iterations is smaller than a threshold value or the maximum iteration frequency is reached. According to the method, the sensitive information in the data set is protected by adding the noise obeying the Laplace distribution to the intermediate parameter in the clustering algorithm execution process; the problem that sensitive information of a data set is leaked in the execution process of the clustering algorithm is solved, the privacy budget allocation mode of the differential privacy protection clustering algorithm is improved, the availability of clustering results is improved under the same privacy protection degree, and the privacy leakage problem inbig data clustering mining is solved.

Description

technical field [0001] The invention relates to a privacy protection clustering method and a computer storage medium, in particular to a privacy protection clustering method and a computer storage medium for big data analysis. Background technique [0002] At present, data mining has been paid more and more attention by people. Using machine learning algorithms to mine and analyze massive data can obtain a lot of valuable new knowledge and new laws. As a commonly used method in the field of data mining, cluster analysis is widely used in scenarios such as data preprocessing, target group classification, pattern recognition, and image segmentation. K-means is the simplest, most effective and most used algorithm in big data clustering analysis. However, during the execution of the algorithm, it is necessary to calculate the number of samples of each cluster and the sum of each attribute when updating the centroid. These operations will leak the data set. Sensitive information...

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Problems solved by technology

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide a privacy-preserving clustering method and computer storage medium for big data analysis, which solves the problem that traditional privacy budget allocation easily leads to excessive random noise, thereby affecting the quality of clustering results , improved the privacy budget allocation method of the differential privacy-preserving clustering algorithm, and proposed a differential privacy budget allocation method, which improves the availability of clustering results under the same degree of privacy protection, and solves the privacy problem in big data cluster mining leak problem

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