A Geospatial Approach to Preserving Location Privacy
Type of DegreePhD Dissertation
Computer Science and Software Engineering
MetadataShow full item record
Sharing true locations of users has become a basic requirement for accessing Location-based services (LBS) on a wide range of web and mobile applications. LBS require users to provide their current location for service delivery and customization. The potential for misuse of true location information by LBS providers and security risks associated with location information falling into wrong hands warrant a pressing need to protect online users’ location privacy. Location privacy protection techniques address 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. However, most of the existing methods for generating dummy locations have problems addressing scenarios where the true location is part of a large parcel area or if the true location is in a remote area with no building structures nearby. In the first part of this dissertation, we propose a novel context-optimized and spatial-aware (COSA) dummy locations generation framework for location privacy, built and evaluated on real-world geospatial data. We evaluated the proposed solution using real-city parcel data and outlined and geo-visualized the results at each step. In the second part of this dissertation, we propose a novel enhanced parcel-based location privacy framework - PLP+ - to construct spatially similar dummy locations anchored on the real-world spatial context of locations such as parcels, building footprints, and road proximity. Our results unveil that PLP+ successfully addresses the map elimination attack in the location set with up to 50 dummy locations by not placing the locations in vacant parcels. Also, there were no dummy locations sharing the same parcel as their true locations out of the 500 dummy locations generated by PLP+, indicating the effectiveness of PLP+ against location homogeneity attack. We develop a novel parameter estimator algorithm for density-based clustering to identify spatial privacy zones within a city. The new algorithm is capable of curtailing the target search area for parcel similarity search from an entire city dataset of 123,848 parcels to a smaller privacy area of 31,412 parcels, with no statistically significant difference in search results. We devise a novel strategy to quantify location privacy by the virtue of building footprint entropy, and we demonstrate that dummy locations generated by PLP+ are consistently higher in footprint entropy offering better location privacy. In the third part of this dissertation, 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 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, VSBDG framework leverages parcel-based similarity, spatial relationships, and operations. Our results show a high physical dispersion cosine similarity of 0.99 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 5861.89 meters for residential locations and 6258.05 meters for POI locations. The findings demonstrate that the locations within each semantic category are scattered farther apart, entailing optimized location privacy.