A user privacy protection method and system for geospatial data

Abstract

The invention provides a geospatial data based user privacy protection method and system. The method comprises: partitioning data space; combining similar cells to the same partition based on a uniformity measurement parameter; adding random noise conforming to Laplace distribution into each partition to obtain a noise data set; and externally providing a data query result based on the noise data set. The invention, based on analysis of noise errors and uniform assumption errors, proposes a novel data field granularity partitioning model for balancing the noise errors and the uniform assumption errors to minimize total data query errors. A condition that query is rectangular query is considered when the model is created, so that an actual data query condition is better met. Furthermore, the similar cells in the data space are combined, so that the query error of the geospatial data is smaller, and the data availability is greatly enhanced while the record security of user privacy is protected.

Description

technical field [0001] The invention belongs to the field of information security and privacy protection, and in particular relates to a user privacy protection method and system for geospatial data. Background technique [0002] The rapid development of the information age has made it easier to obtain data, such as user's mobile trajectory data, shopping record data, and office / home address data. All these data composed of location points are called geospatial data. Through the analysis of geospatial data, a lot of useful information can be obtained. For the transportation department, the current traffic conditions can be obtained by analyzing the user's movement trajectory data, and provide data support for urban traffic control; for the merchants, the user's shopping record data or vehicle movement trajectory data can be analyzed to obtain the areas or clusters that the user is interested in To provide data support for commercial layout; for the urban planning bureau, it...

AI Technical Summary

Problems solved by technology

For two-dimensional geospatial data, when the data domain is segmented through the tree structure to improve data availability, the tree depth is the key factor affecting the data query results, and the above literature does not provide theoretical support for the specific tree depth or partition granularity

Starting from the granularity of the data domain, Qardaji et al. studied how to construct a differentially private data set, and proposed a granularity partition model based on noise error and uniform assumption error, which opened up a new direction to improve data availability. The query shape is square, the length is equal to the width, which does not conform to the actual situation of data query, and the uniform assumption error is set to be proportional to the total number of points in the query boundary cells, without considering the cell area factor

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