A sweepline algorithm for Voronoi diagrams
SCG '86 Proceedings of the second annual symposium on Computational geometry
Framework for security and privacy in automotive telematics
WMC '02 Proceedings of the 2nd international workshop on Mobile commerce
An Overview of Location-Based Services
BT Technology Journal
Location Privacy in Pervasive Computing
IEEE Pervasive Computing
Preserving Privacy in Environments with Location-Based Applications
IEEE Pervasive Computing
Selective partial access to a database
ACM '76 Proceedings of the 1976 annual conference
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Landscape-aware location-privacy protection in location-based services
Journal of Systems Architecture: the EUROMICRO Journal
A survey of computational location privacy
Personal and Ubiquitous Computing
Privacy-Aware Proximity Based Services
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
An Obfuscation-Based Approach for Protecting Location Privacy
IEEE Transactions on Dependable and Secure Computing
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
PPREM: Privacy Preserving REvocation Mechanism for Vehicular Ad Hoc Networks
Computer Standards & Interfaces
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As location-based services emerge, many people feel exposed to high privacy threats. Privacy protection is a major challenge for such applications. A broadly used approach is perturbation, which adds an artificial noise to positions and returns an obfuscated measurement to the requester. Our main finding is that, unless the noise is chosen properly, these methods do not withstand attacks based on probabilistic analysis. In this paper, we define a strong adversary model that uses probability calculus to de-obfuscate the location measurements. Such a model has general applicability and can evaluate the resistance of a generic location-obfuscation technique. We then propose UniLO, an obfuscation operator which resists to such an adversary. We prove the resistance through formal analysis. We finally compare the resistance of UniLO with respect to other noise-based obfuscation operators.