A privacy protection query method based on Voronoi polygons and Hilbert curve coding

Abstract

The invention relates to a privacy protection query method based on Voronoi polygons and Hilbert curve coding. The method comprises the steps that a server end performs Voronoi polygon partitioning on a plane where a target object is located, performs partitioning coding by using the Hilbert curve, and establishes a B+ tree index mapping the Hilbert cell coding and Voronoi polygons; a user submits a Hilbert curve coding value H (p) of a self-position p to the server via a client for k-neighbor query; the server end searches for a Voronoi polygon C corresponding to the H(p) on the query tree to generate the minimum enclosing rectangle R of C; the server end searches for k-1 neighbor Voronoi polygons of R, forms Hilbert curve coding values corresponding to the Voronoi polygons into a candidate query result set CaS and returns the result to the client; the user decodes the Hilbert curve coding values in the CaS to screen out the nearest neighbor target object. The method achieves k-neighbor query for protecting position privacy.

Description

technical field [0001] The invention relates to a query method, in particular to a privacy protection query method based on Voronoi polygon and Hilbert curve coding, and belongs to the technical field of data query. Background technique [0002] In recent years, services based on location information have been widely used in computer application fields, such as GPS navigation, spatial information query and so on. Most of these applications are based on k-nearest neighbor query. The mode is that the service provider stores all information (including location information) of objects of interest to the user on the server, and the user configures a device that can obtain the location information (2-dimensional latitude and longitude coordinates) of the user ( For example, GPS terminal), by sending the user's current location to the service provider, the server returns the relevant information of the k objects closest to the user's current location. This model requires users to ...

AI Technical Summary

Problems solved by technology

The method based on location obstruction does not require the participation of a trusted third party. The client sends a fake location, and the server continuously sends the neighbors of the fake location for the client to detect until the client finds k neighbors that meet the user's requirements. The method has the problem that the query iteration process is uncontrollable, and like most privacy-preserving query methods, it has the problem of low query processing efficiency

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