Automobile Driver Fingerprinting

Miro Enev 1 , Alex Takakuwa 1 , Karl Koscher 2  and Tadayoshi Kohno
  • 1 University of Washington
  • 2 University of California, San Diego


Today’s automobiles leverage powerful sensors and embedded computers to optimize efficiency, safety, and driver engagement. However the complexity of possible inferences using in-car sensor data is not well understood. While we do not know of attempts by automotive manufacturers or makers of after-market components (like insurance dongles) to violate privacy, a key question we ask is: could they (or their collection and later accidental leaks of data) violate a driver’s privacy? In the present study, we experimentally investigate the potential to identify individuals using sensor data snippets of their natural driving behavior. More specifically we record the in-vehicle sensor data on the controllerarea- network (CAN) of a typical modern vehicle (popular 2009 sedan) as each of 15 participants (a) performed a series of maneuvers in an isolated parking lot, and (b) drove the vehicle in traffic along a defined ~ 50 mile loop through the Seattle metropolitan area. We then split the data into training and testing sets, train an ensemble of classifiers, and evaluate identification accuracy of test data queries by looking at the highest voted candidate when considering all possible one-vs-one comparisons. Our results indicate that, at least among small sets, drivers are indeed distinguishable using only incar sensors. In particular, we find that it is possible to differentiate our 15 drivers with 100% accuracy when training with all of the available sensors using 90% of driving data from each person. Furthermore, it is possible to reach high identification rates using less than 8 minutes of training data. When more training data is available it is possible to reach very high identification using only a single sensor (e.g., the brake pedal). As an extension, we also demonstrate the feasibility of performing driver identification across multiple days of data collection

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  • [1] Apple carplay.

  • [2] Automatic.

  • [3] Buyer beware: Ez pass toll tags used by private attorneys to catch cheating spouses.

  • [4] Epic comments on electronic data recorders.

  • [5] Exclusive: Say Goodbye to Chip Tuning Open CAN Bus Going Away in Two Model Cycles.

  • [6] Inverse discrete stationary wavelet transform 1-d.

  • [7] Markey report reveals automobile security and privacy vulnerabilities.

  • [8] The next data privacy battle may be waged inside your car.

  • [9] Privacy of data from event data recorders: State statutes, national conference of state legislatures.

  • [10] Progressive snapshot privacy statement.

  • [11] Teletrac.

  • [12] Tesla CEO Elon Musk disputes N.Y. Times article on Model S range.

  • [13] Wikipedia: Usage based insurance.

  • [14] Zubie.

  • [15] Arkansas Code Title 23, Chapter 112, Section 107. Motor vehicle event data recorder - data ownership.

  • [16] S. Choi, J. Kim, D. Kwak, P. Angkititrakul, and J. H. L. Hansen. Analysis and classification of driver behavior using in-vehicle can-bus information.

  • [17] R. R. Coifman and D. L. Donoho. Translation-invariant de-noising. Springer, 1995.

  • [18] Connecticut General Statutes Chapter 246b. Motor vehicle event data recorders.

  • [19] C. Dwork. Differential privacy. In Encyclopedia of Cryptography and Security, pages 338-340. Springer, 2011.

  • [20] M. Enev, J. Jung, L. Bo, X. Ren, and T. Kohno. Sensorsift: balancing sensor data privacy and utility in automated face understanding. In Proceedings of the 28th Annual Computer Security Applications Conference, pages 149-158. ACM, 2012.

  • [21] T. Flash and N. Hogan. The coordination of arm movements: an experimentally confirmed mathematical model. The journal of Neuroscience, 5(7):1688-1703, 1985.

  • [22] N. Kawaguchi, S. Matsubara, K. Takeda, and F. Itakura. Multimedia data collection of in-car speech communication. In 7th European Conference on Speech Communication and Technology/2nd INTERSPEECH Event in Aalborg, Denmark on September 3-7, 2001 (EUROSPEECH 2001). 2001, p. 2027-2030, 2001.

  • [23] C. Miyajima, Y. Nishiwaki, K. Ozawa, T. Wakita, K. Itou, K. Takeda, and F. Itakura. Driver modeling based on driving behavior and its evaluation in driver identification. Proceedings of the IEEE, 95(2):427-437, 2007.

  • [24] Y. Nishiwaki, K. Ozawa, T. Wakita, C. Miyajima, K. Itou, and K. Takeda. Driver identification based on spectral analysis of driving behavioral signals. In Advances for In-Vehicle and Mobile Systems, pages 25-34. Springer US, 2007.

  • [25] Oregon Revised Statutes Chapter 105 Motor Vehicle Event Data Recorders. Retrieval or use of data for responding to medical emergency, for medical research or for vehicle servicing or repair.

  • [26] S.-H. Park and J. Fürnkranz. Efficient pairwise classification. In Machine Learning: ECML 2007, pages 658-665. Springer, 2007.

  • [27] L. Sweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557-570, 2002.

  • [28] M. Van Ly, S. Martin, and M. M. Trivedi. Driver classification and driving style recognition using inertial sensors. In Intelligent Vehicles Symposium (IV), 2013 IEEE, pages 1040-1045. IEEE, 2013.

  • [29] T. Wakita, K. Ozawa, C. Miyajima, K. Igarashi, I. Katunobu, K. Takeda, and F. Itakura. Driver identification using driving behavior signals. IEICE TRANSACTIONS on Information and Systems, 89(3):1188-1194, 2006.

  • [30] M. Wolf, A. Weimerskirch, and C. Paar. Security in automotive bus systems. In Workshop on Embedded Security in Cars, 2004.

  • [31] X. Zhang, X. Zhao, and J. Rong. A study of individual characteristics of driving behavior based on hidden markov model. Sensors & Transducers (1726-5479), 167(3), 2014.


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