Elastic Pathing: Your Speed is Enough to Track You

Email: info@elasticpathing.org

Watch the Demo Video on Youtube!

Our paper "Elastic Pathing: Your Speed is Enough to Track You" received the Best Paper Nominee Award (4% of all papers) at UbiComp'14!

Today people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely knowing their home location, as insurance companies know, and speed data with an accuracy that constitutes privacy intrusions. To demonstrate the algorithm’s real-world applicability, we evaluated its performance with datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. Our algorithm predicted destinations with error within 250 meters for 14% traces and within 500 meters for 24% traces in the New Jersey dataset (254 traces). For the Seattle dataset (691 traces), we similarly predicted destinations with error within 250 and 500 meters for 13% and 26% of the traces respectively. Our work shows that these insurance schemes enable a substantial breach of privacy.



Click following links to download our source code and traces.

How to use these files

There are two testing datasets we used for testing:

The source code includes following:

All source code are documented. Please see the README files within the package for further instructions. Our resources are free to use for non-commercial research or educational purposes. You must not attempt to deanonymize the participants from our dataset. If you publish your results based on our code or dataset, please cite our Elastic Pathing paper in UbiComp 2014.


Janne Lindqvist - Rutgers University


Xianyi Gao - Rutgers University

Bernhard Firner - Rutgers University

Shridatt Sugrim - Rutgers University

Victor Kaiser-Pendergrast - Rutgers University

Yulong Yang - Rutgers University


This material is based upon work supported by the National Science Foundation under Grant Numbers 1228777 and 1211079. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.