Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Revisiting the uniqueness of simple demographics in the US population
Proceedings of the 5th ACM workshop on Privacy in electronic society
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
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The collection of driving habits data is gaining momentum as vehicle telematics based solutions become popular in consumer markets such as auto-insurance and driver assistance services. These solutions rely on driving features such as time of travel, speed, and braking to assess accident risk and driver safety. Given the privacy issues surrounding the geographic tracking of individuals, many solutions explicitly claim that the customer's GPS coordinates are not recorded. Although revealing driving habits can give us access to a number of innovative products, we believe that the disclosure of this data only offers a false sense of privacy. Using speed and time data from real driving trips, we show that the destinations of trips may also be determined without having to record GPS coordinates. Based on this, we argue that customer privacy expectations in non-tracking telematics applications need to be reset, and new policies need to be implemented to inform customers of possible risks.