Multi-way tree forest K-anonymity method and system, equipment and readable storage medium

Abstract

The embodiment of the invention discloses a multi-way tree forest K-anonymity method and system, equipment and a readable storage medium. On the basis of calculation of the recognition rate, the problem of each step of optimal generalization data selection of a greedy algorithm in a local generalization mode is solved, a multi-way tree forest is established to achieve storage and local generalization of generalization data, and the efficiency of the model is improved through multi-thread generalization data selection and K-anonymity parallel calculation judgment. That is to say, part of data is generalized on the basis of original data, so that an anonymous data set meets a K-anonymity model. The authenticity of the data is not influenced under the condition of ensuring the data security.Meanwhile, the model reserves the distribution of the data, improves the availability of the data after data anonymization, and is suitable for anonymization processing of a small data set and a largedata set.

Description

technical field [0001] The embodiment of the present application relates to the technical field of information security, and specifically relates to a multi-fork forest K-anonymity method, system, device and readable storage medium. Background technique [0002] With the acceleration of the pace of digitization in the information age and the popularization and application of big data, technologies such as data mining have brought huge knowledge benefits, making data collection more important. However, due to the immaturity of relevant laws and technologies, When information is shared and applied, privacy is leaked, which brings potential security risks to privacy stakeholders. The security issue in data release has gradually become an urgent problem to be solved. [0003] As one of the data desensitization models proposed earlier, the K-anonymity algorithm has many good properties for data security protection, but it also has shortcomings. K anonymity will cause informatio...

AI Technical Summary

Problems solved by technology

K anonymity will cause information loss caused by anonymization, resulting in poor data availability, and it will take a lot of time for data changes

This type of algorithm has many limitations. First of all, if a certain attribute is selected for generalization, if K-anonymity is not satisfied, it may cause over-generalization of the attribute, resulting in loss of information in the data set.

Secondly, global generalization is generally difficult to meet the availability requirements of anonymized data

In addition, it is impossible to accurately measure whether the best anonymity effect is achieved in each generalization, so as to improve the efficiency of the algorithm as a whole

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