Mining of spatial data is an enabling technology for mobile services, Internet-connected cars and the Internet of Things. But the very distinctiveness of spatial data that drives utility can cost user privacy. Past work has focused upon points and trajectories for differentially private release. In this work, we continue the tradition of privacy-preserving spatial analytics, focusing not on point or path data, but on planar spatial regions. Such data represent the area of a user’s most frequent visitation—such as “around home and nearby shops”. Specifically we consider the differentially private release of data structures that support range queries for counting users’ spatial regions. Counting planar regions leads to unique challenges not faced in existing work. A user’s spatial region that straddles multiple data structure cells can lead to duplicate counting at query time. We provably avoid this pitfall by leveraging the Euler characteristic for the first time with differential privacy. To address the increased sensitivity of range queries to spatial region data, we calibrate privacy-preserving noise using bounded user region size and a constrained inference that uses robust least absolute deviations. Our novel constrained inference reduces noise and promotes covertness by (privately) imposing consistency. We provide a full end-to-end theoretical analysis of both differential privacy and high-probability utility for our approach using concentration bounds. A comprehensive experimental study on several real-world datasets establishes practical validity.
Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified. Table of Contents: Introduction Privacy-Preserving Spatial Transformations Cryptographic Approaches Hybrid Approaches Private Matching of Spatial Datasets Trajectory Anonymization Differentially Private Publication of Spatial Datasets Conclusions The ubiquity of mobile devices with global positioning functionality (e.g., GPS and Assisted GPS) and Internet connectivity (e.g., 3G and Wi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's a sequence of location samples to infer the user's location information with a higher degree of certainty. Such user spatial trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing original spatial trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this chapter, we describe the state-of-the-art privacy-preserving techniques for continuous LBS and trajectory publication. This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats. Although some users might willingly subscribe to location-tracking services, few would be comfortable having their location known in all situations. The authors investigate disclosure-control algorithms that hide users' positions in sensitive areas and withhold path information that indicates which areas they have visited. Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects' home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects.