k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
ICMB '08 Proceedings of the 2008 7th International Conference on Mobile Business
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Privacy vulnerability of published anonymous mobility traces
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Unraveling an old cloak: k-anonymity for location privacy
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Quantifying location privacy: the case of sporadic location exposure
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
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Privacy preserving mechanisms help users of location-based services to balance their location privacy while still getting useful results from the service. The provided location privacy depends on the users' behavior and an adversary's knowledge used to locate the users. The aim of this paper is to investigate how users' mobility patterns and adversaries' knowledge affect the location privacy of users querying a location-based service. We consider three mobility trace models in order to generate user traces that cross each other, are parallel to each other and form a circular shape. Furthermore, we consider four adversary models, which are distinguished according to their level of knowledge of users. We simulate the trace and the adversary models by using Distortion-based Metric and K-anonymity. The results show that the location privacy provided by K-anonymity decreases, as users are located closer to each other in the trace models. The impact of the adversary on location privacy is reduced as more users are cloaked together.