The Long Road to Computational Location Privacy: A Survey

Abstract

The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.

arXiv commentIEEE Communications Surveys & Tutorials

References in corpus (10)

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• Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services
• Mining User Behaviour from Smartphone data: a literature review
• Reconstruction Attack on Differential Private Trajectory Protection Mechanisms
• On (The Lack Of) Location Privacy in Crowdsourcing Applications
• PGLP: Customizable and Rigorous Location Privacy through Policy Graph
• Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data
• On the Data Fight Between Cities and Mobility Providers
• Learning interaction rules from multi-animal trajectories via augmented behavioral models
• Synthetic Trajectory Generation Through Convolutional Neural Networks
• Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
• PANDA: Policy-aware Location Privacy for Epidemic Surveillance
• Demystifying Trajectory Recovery From Ash: An Open-Source Evaluation and Enhancement