A method and system for realizing data set desensitization based on k-anonymity algorithm

Abstract

The invention discloses a method and system for desensitizing a data set based on a k-anonymity algorithm, comprising the following steps: obtaining a data set that has not been desensitized; deleting the display identifier of the data set; Each attribute takes the order of the value domain to make it an ordered domain; maps the ordered domain to the real number domain one by one; defines the distance of each data point in space and calculates the relative distance, and divides it according to the relative distance combined with the projection area density The algorithm determines the division point of the data set, calculates the division points at all levels recursively, and finally establishes the hypersphere group; generalizes the point information contained in each hypersphere in the hypersphere group, so that the accuracy of all records The values ​​of the identifiers are the same, and the desensitization process is completed. The present invention can improve the area corner problem existing in the rectangle, and can take into account the distance between adjacent points in the temporary anonymous group, so that more anonymous groups can be obtained under the premise of ensuring privacy protection, and the degree of generalization of data can be improved. The lower the value, the greater the availability of data.

Description

technical field [0001] The present invention relates to the technical field of data desensitization, and more specifically, to a method and system for desensitizing data sets based on a k-anonymity algorithm. Background technique [0002] The common processing method of privacy data anonymization originates from the data processing method in the statistical database, mainly through the information loss of the attribute values ​​in the published data in exchange for the accuracy of re-identifying some individuals through these attribute values, and at the same time It is possible to guarantee the availability of published data, and to achieve a balance between the accuracy of published data and privacy protection. [0003] As far as the current technology is concerned, the division strategy for anonymous groups disclosed in [1] is "anonymous algorithm based on rounding and division" (RPF) and "k-anonymous algorithm based on vertex and edge modification" is disclosed in docume...

AI Technical Summary

Problems solved by technology

[0017] In order to solve the problem that the prior art does not take into account the distance of adjacent data in the temporary anonymous group, data information loss is likely to occur when generating a data set, and the existing segmentation is a rectangle formed with area corners, thereby reducing the usability of the obtained data To solve this problem, a method and system for desensitizing data sets based on the k-anonymity algorithm is provided, which can solve the problem of area corners, and under the premise of protecting privacy, the more anonymous groups of data, the more generalized the data The lower the rate, the greater the availability of data

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