A Voronoi-Based Semantically Balanced Dummy Generation Framework for Location Privacy

Abstract

Location-based services (LBS) require users to provide their current location for service delivery and customization. Location privacy protection addresses concerns associated with the potential mishandling of location information submitted to the LBS provider. Location accuracy has a direct impact on the quality of service (QoS), where higher location accuracy results in better QoS. In general, the main goal of any location privacy technique is to achieve maximum QoS while providing minimum or no location information if possible, and using dummy locations is one such location privacy technique. In this paper, we introduced a temporal constraint attack whereby an adversary can exploit the temporal constraints associated with the semantic category of locations to eliminate dummy locations and identify the true location. We demonstrated how an adversary can devise a temporal constraint attack to breach the location privacy of a residential location. We addressed this major limitation of the current dummy approaches with a novel Voronoi-based semantically balanced framework (VSBDG) capable of generating dummy locations that can withstand a temporal constraint attack. Built based on real-world geospatial datasets, the VSBDG framework leverages spatial relationships and operations. Our results show a high physical dispersion cosine similarity of 0.988 between the semantic categories even with larger location set sizes. This indicates a strong and scalable semantic balance for each semantic category within the VSBDG’s output location set. The VSBDG algorithm is capable of producing location sets with high average minimum dispersion distance values of 5861.894 m for residential locations and 6258.046 m for POI locations. The findings demonstrate that the locations within each semantic category are scattered farther apart, entailing optimized location privacy.