Differential privacy protection-oriented k-means clustering method adopting

Abstract

The invention discloses a differential privacy protection-oriented k-means clustering method. The K-means clustering method comprises the following steps: performing data preprocessing; ensuring thatC indicates a clustered centered point set, and C indicates a sum of error square of a given data set and a cluster center C; judging the volume of C; performing cyclic execution until retry is greater than a maximum value retrymatx of given retry times, and then returning to the best central point Cbest; traversing each point of the data set X, classifying the points to the nearest central point;setting added random noises; renewedly calculating the sum of the data points of each cluster and the quantity of the points, and adding the noises and finally updating the quality center of the cluster; and repeatedly carrying out the steps until the sum of error square is converged or iteration times reach the upper limit. According to the differential privacy protection-oriented k-means clustering method disclosed by the invention, the appropriate random noises which are specially distributed are added in an iteration process of a k-means clustering algorithm, so that a clustering result is distorted to a certain extent, the aim of privacy protection is fulfilled, and meanwhile, the availability of data is ensured.

Description

technical field [0001] The invention relates to a privacy protection and clustering method, in particular to a differential privacy protection-oriented k-means clustering method, which belongs to the technical field of information security. Background technique [0002] With the rapid development of cloud computing and big data, data mining technology has made great progress in some in-depth research and applications. As one of the important methods of data mining, clustering algorithm can mine implicit and unknown knowledge and rules, and has important potential value in business decision-making of a large amount of relevant data. But at the same time, a large amount of information disclosure of sensitive information brings immeasurable threats and losses to users. Therefore, how to protect data privacy in the process of cluster analysis has become a hot issue in the field of data mining and data privacy protection. With the proposal and development of privacy protection ...

AI Technical Summary

Problems solved by technology

However, traditional differential privacy-protected k-means algorithms (such as differential privacy k-means algorithm, differential privacy k-means++ algorithm, etc.) blindness, which reduces the availability of clustering results

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