Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
TrafficView: traffic data dissemination using car-to-car communication
ACM SIGMOBILE Mobile Computing and Communications Review
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Swing & swap: user-centric approaches towards maximizing location privacy
Proceedings of the 5th ACM workshop on Privacy in electronic society
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
On the effectiveness of changing pseudonyms to provide location privacy in VANETS
ESAS'07 Proceedings of the 4th European conference on Security and privacy in ad-hoc and sensor networks
Achieving Guaranteed Anonymity in GPS Traces via Uncertainty-Aware Path Cloaking
IEEE Transactions on Mobile Computing
Mobeyes: smart mobs for urban monitoring with a vehicular sensor network
IEEE Wireless Communications
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Efforts to anonymize collections of location traces have often sought to reduce re-identification risks by dividing longer traces into multiple shorter, unlinkable segments. To ensure unlinkability, these algorithms delete parts from each location trace in areas where multiple traces converge, so that it is difficult to predict the movements of any one subject within this area and identify which follow-on trace segments belongs to the same subject. In this paper, we ask whether it is sufficient to base the definition of unlinkability on movement prediction models or whether the revealed trace segments themselves contain a fingerprint of the data subject that can be used to link segments and ultimately recover private information. To this end, we study a large set of vehicle locations traces collected through the Next Generation Simulation program. We first show that using vehicle moving characteristics related features, it is possible to identify outliers such as trucks or motorcycles from general passenger automobiles. We then show that even in a dataset containing similar passenger automobiles only, it is possible to use outlier driving behaviors to link a fraction of the vehicle trips. These results show that the definition of unlinkability may have to be extended for very precise location traces.