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To date, location privacy protection is a critical issue in Location-BasedServices (LBS). In this work, we propose a novel geometric framework based onthe classical discrete geometric structure, the Voronoi-Delaunay duality (VDD).We utilize the fact that the user location cannot be recovered if only given anirregular $n$-sided Voronoi cell around it, and the anonymity zone is theintersection of all the parallel strips perpendicular to and bounded by $n$Voronoi edges. The irregular Voronoi cell and its variations can be used as theconcealing space to hide the user location or the region of interest andsubmitted to the LBS server. Within this framework, we propose multiple typicalanonymizing models by introducing irregularity to the convex regular VDDstructure by shifting the interior Voronoi cell, exterior Delaunay polygon,sector rays, or their combinations. The proposed methods are efficient bytaking advantage of the VDD principle where main computations are linearline-line intersections. Experiments with various parameters demonstrate theefficiency and efficacy of the proposed $n$-VDD framework.

$n$-VDD: Location Privacy Protection Based on Voronoi-Delaunay Duality