Differential privacy protection-oriented k-means clustering method adopting

A differential privacy and clustering method technology, applied in the field of information security, can solve problems such as reducing the availability of clustering results, avoid the blindness of k value and initial point sensitivity, improve usability, and reduce the number of iterations.

CN108280491AActive Publication Date: 2018-07-13DONGGUAN MENGDA INDAL INVESTMENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2018-07-13

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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.
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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 ...

Examples

Embodiment Construction

[0036] The implementation of the technical solution of the present invention will be described in further detail below in conjunction with the accompanying drawings. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. After reading the present invention, those skilled in the art will understand the present invention Modifications in various equivalent forms fall within the scope defined by the appended claims of the present application.

[0037] A differential privacy-oriented k-means clustering method of the present invention uses the result of the k-means++ algorithm as an input value, and then performs a series of non-local "jumps" alternately with the traditional k-means algorithm to improve the initial The selection of the center point, and using the differential privacy protection Laplace mechanism, increases the appropriate random noise that satisfies a specific di...