Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
On the anonymity of periodic location samples
SPC'05 Proceedings of the Second international conference on Security in Pervasive Computing
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
A spatiotemporal model of strategies and counter strategies for location privacy protection
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Faking contextual data for fun, profit, and privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Protecting location privacy against inference attacks
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
On the limitations of query obfuscation techniques for location privacy
Proceedings of the 13th international conference on Ubiquitous computing
Cover locations: availing location-based services without revealing the location
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Personalized driving behavior monitoring and analysis for emerging hybrid vehicles
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
A dummy-based anonymization method based on user trajectory with pauses
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Location anonymization using real car trace data for location based services
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Simulated, false location reports can be an effective way to confuse a privacy attacker. When a mobile user must transmit his or her location to a central server, these location reports can be accompanied by false reports that, ideally, cannot be distinguished from the true one. The realism of the false reports is important, because otherwise an attacker could filter out all but the real data. Using our database of GPS tracks from over 250 volunteer drivers, we developed probabilistic models of driving behavior and applied the models to create realistic driving trips. The simulations model realistic start and end points, slightly non-optimal routes, realistic driving speeds, and spatially varying GPS noise.