Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Privacy and location anonymization in location-based services
SIGSPATIAL Special
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Achieving Guaranteed Anonymity in GPS Traces via Uncertainty-Aware Path Cloaking
IEEE Transactions on Mobile Computing
Privacy vulnerability of published anonymous mobility traces
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Empirical models of privacy in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
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One common assumption when defining location privacy metrics is that one is dealing with attackers who have the objective of re-identifying an individual out of an anonymized data set. However, in today's communication scenarios, user communication and information exchange with (partially) trusted peers is very common, e.g., in communication via social applications. When disclosing voluntarily a single observation to a (partially) trusted communication peer, the user's privacy seems to be unharmed. However, location data is able to transport much more information than the simple fact of a user being at a specific location. Hence, a user-centric privacy metric is required in order to measure the extent of exposure by releasing (a set of) location observations. The goal of such a metric is to enable individuals to estimate the privacy loss caused by disclosing further location information in a specific communication scenario and thus enabling the user to make informed choices, e.g., choose the right protection mechanism.