K-cryptonym improving method

Abstract

The invention discloses a K-cryptonym improving method, relating to a data mining field. The K-cryptonym improving method comprising the following steps of selecting a quasi-identifier according to an original dataset; determining a generalizing mode and establishing an initial generalizing lattice corresponding to the generalizing mode; judging whether the initial generalizing lattice is empty or not; if not, selecting a global optimum node from all nodes of the initial generalizing lattice according to the optimum node selection mode and obtaining a first generalizing lattice; carrying out the cryptonym processing on the data to be issued according to the global optimum node and obtaining the quantity of cryptonym cluster; judging whether the quantity of the cryptonym cluster is less than the prearranged quantity or not; if so, carrying out the optimum node selection mode calculation on the first generalizing lattice and obtaining the optimum node; if not, carrying out the secondary K-cryptonym calculation to the first generalizing lattice and obtaining the optimum node as the cryptonym cluster is an isolated cluster; generalizing the data to be issued according to the generalizing mode corresponding to the optimum node; and issuing the generalized data. By adopting the K-cryptonym improving method, the execution time is shortened, and the information accuracy is improved.

Description

technical field [0001] The invention relates to K-anonymity in the field of data mining, in particular to an improved K-anonymity method. Background technique [0002] The commonly used processing methods for data anonymization are derived from the data processing methods in statistical databases, mainly through the loss of information on 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. Traditional privacy protection methods often sacrifice the accuracy of individual data records in order to ensure the overall trend of published data. [0003] K-Anonymity (K-anonymity algorithm): K-anonymity (K-Anonymity) is different from traditional access control and other goal-based privacy protection technologies. It is a typ...

AI Technical Summary

Problems solved by technology

[0011] The K-anonymity method in the prior art needs to compare all the nodes in the generalization grid when judging and comparing. When the scale of the generalization grid is relatively large, the execution time will be very long, which is very important for data processing. Unfavorable; this method is mostly globally optimal. Due to the inhomogeneity of data distribution, that is, there are isolated clusters (that is, a small number of sets), in order to meet the anonymous requirements, a higher generalization level has to be adopted. This obviously reduces the accuracy of the information

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