Privacy protection method and system based on high-risk frequent active point replacement policy

Abstract

The invention provides a privacy protection method and system based on a high-risk frequent active point replacement policy. The privacy protection method comprises the following steps: establishing a privacy model, grading individual risks into zero-risk individuals, low-risk individuals and high-risk individuals based on the privacy model, hiding the high-risk individuals in the low-risk individuals for privacy protection, and evaluating a privacy protection result. The method is simple, is low in calculation complexity and can be applicable to spatial data with large data sizes.

Description

technical field [0001] The technical field of data security of the present invention specifically relates to a privacy protection method and system based on a replacement strategy for high-risk frequent activity points. Background technique [0002] In the context of big data, it has become possible to identify individuals from call location data (CDR) by mining the spatial data of mobile phone user activities. With its detailed record list and powerful information contained in mobile phone positioning data, it has become a main research direction of various operators and scientific research institutions. At present, the identification of individual occupation and residence through mobile phone data, and the study of urban residents' activity patterns and urban spatial structure through mobile phone data have become a reality and have been scientifically verified. From the released mobile phone positioning data, using related mining algorithms to identify individuals with o...

AI Technical Summary

Problems solved by technology

The method based on the entire trajectory data consumes a lot of resources and time, and it is difficult to guarantee the overall distortion of the data

At the same time, this protection method based on trajectory fragments has complex operation procedures and high computational complexity, making it difficult to deal with large amounts of spatial data.

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