Preserving Privacy in Environments with Location-Based Applications
IEEE Pervasive Computing
Access control to people location information
ACM Transactions on Information and System Security (TISSEC)
Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
IEEE Transactions on Mobile Computing
Privacy in Location-Aware Computing Environments
IEEE Pervasive Computing
Realistic Driving Trips For Location Privacy
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
A survey of computational location privacy
Personal and Ubiquitous Computing
Faking contextual data for fun, profit, and privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Virtual walls: protecting digital privacy in pervasive environments
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
| Hi-index | 0.00 |
GPS-enabled mobile devices are a quickly growing market and users are starting to share their location information with each other through services such as Google Latitude. Location information, however, is very privacy-sensitive, since it can be used to infer activities, preferences, relationships, and other personal information, and thus access to it must be carefully protected. The situation is complicated by the possibility of inferring a users' location information from previous (or even future) movements. We argue that such inference means that traditional access control models that make a binary decision on whether a piece of information is released or not are not sufficient, and new policies must be designed that ensure that private information is not revealed either directly or through inference. We provide a formal definition of location privacy that incorporates an adversary's ability to predict location and discuss possible implementation of access control mechanisms that satisfy this definition. To support our reasoning, we analyze a preliminary data set to evaluate the accuracy of location prediction.