Multi-view clustering and mining oriented personal privacy protection method

Abstract

The invention discloses a multi-view clustering and mining oriented personal privacy protection method, and belongs to the technical field of information safety. The multi-view clustering and mining oriented personal privacy protection method has the advantages that privacy partial-order topological classification algorithms (PT, privacy topology) are proposed, privacy relations are defined at first for representing sensitivity difference of different privacy data, privacy partial-order sets are constructed for representing the sensitivity difference of the different privacy data, topologicalclassification algorithms are accordingly designed for the privacy data, and privacy line order sets are solved; multi-view clustering is carried out on views of original data, privacy degrees, tuplesensitivity, the privacy line order sets and the like for multiple views of the privacy data; clustering oriented personal anonymity algorithms (PPOC, personal privacy oriented classtering) are proposed, personal protection operation can be carried out on different clusters by variable k-anonymity strategies by the aid of multi-view clustering oriented privacy protection algorithms which can meetpersonal requirements, and personal protection operation with different exertion degrees can be carried out on different tuples in the same clusters by the variable k- anonymity strategies by the aidof the multi-view clustering oriented privacy protection algorithms.

Description

technical field [0001] The invention relates to a personalized privacy protection method for multi-view cluster mining, and relates to the technical field of information security. Background technique [0002] With the development of digital technologies such as the Internet, the Internet of Things, and smart cities, various data collection devices such as sensors and mobile terminals store various information about human clothing, food, housing, and transportation in digital form, thus giving birth to the era of big data. For the first time, data, as a resource, has received great attention from social entities such as governments, enterprises, and academia. [0003] However, in the process of data use, personal privacy information may be leaked. In the process of discovering the potential value of data, how to protect the privacy of individuals, especially how to avoid privacy leakage caused by data mining, is a key issue that data science needs to solve urgently. Privat...

AI Technical Summary

Problems solved by technology

[0007] (1) Existing algorithms are almost designed for the needs of data release, and have not been involved in privacy and security issues that may be caused by cluster mining;

[0008] (2) The existing personalized privacy protection algorithm does not comprehensively consider the differences in the privacy awareness of users and the importance of different attributes, the degree of personalization needs to be improved, and the information loss is relatively large

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