Data anonymization device and method

Abstract

The invention provides a data anonymization device and a data anonymization method. The device comprises a distance calculation unit, a full connection diagram construction unit, a side removing unit, a large tree decomposition unit and a generalization unit, wherein the distance calculation unit is used for calculating distances among a plurality of data records; the full connection diagram construction unit is used for using the records as apexes, connecting all the apexes pairwise, using the distances among the records as weights of sides among the corresponding apexes, and constructing a full connection diagram comprising all the records; the side removing unit is used for sequentially removing the sides according to the weight of each side, dividing the full connection diagram into a plurality of trees and making each tree comprise at least k apexes; the large tree decomposition unit is used for further decomposing trees with the apex number of more than (2k-1), so that the number of apexes contained in each decomposed subtree is more than or equal to k and less than or equal to (2k-1); and the generalization unit is used for generalizing records corresponding to all the apexes in each finally obtained tree, so that the records in the tree cannot be distinguished. By the data anonymization device and the data anonymization method, information loss is further reduced through a global optimization mechanism.

Description

technical field [0001] The invention relates to the technical field of data protection, in particular to a data anonymization device and method. Background technique [0002] With the development of social informatization, data sharing has become increasingly common, but attackers may obtain or infer private information of individuals or organizations from each item of shared data records, which requires protection of personal privacy and organizational secrets, for example. data poses a security threat. [0003] Generally speaking, the records contained in various data records (such as data records in the form of tables) can be roughly divided into four categories: explicit identification, such as personal name, ID number, company registered name, etc., which can directly identify the object ;Quasi-identification, such as the age, gender, education, place of birth, company category, location, etc., of the individual, combined with relevant external information, can infer t...

AI Technical Summary

Problems solved by technology

[0012] In the above-mentioned existing method using the bottom-up process, in the construction of the tree, the selection of vertices and their neighbors is random without any order control mechanism. When the number of vertices is greater than max{2k-1, 3k- 5}'s graph (also known as a large tree) does not take into account the information loss optimization problem; and these methods mainly consider the local optimization of information loss, but do not consider the overall situation involving all records or vertices optimization

Although this kind of local optimization can reduce information loss to a certain extent, it cannot achieve global optimization because it does not take into account the overall situation. The information loss caused by this is still unacceptable for the current demanding follow-up data analysis.

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