Master’s Thesis: De-anonymization of Vehicle Traces with Variant Pseudonyms (PDF)
Future vehicle is connected and cooperative, whereby its intelligent sensors, real-time control and communication networks enable cars to communicate together or with infrastructure. Connected and cooperative cars (aka. Vehicular Ad hoc Network (VANET) or Car2X Communication) open the door for more intelligent applications regarding safety, traffic efficiency and infotainment. Most of these applications will depend on frequently sharing the current state of a vehicle such as a precise location, speed and heading. However, sharing this information should be carefully handled since it may threaten the driver’s privacy. Such movement information, when collected and analyzed, can expose sensitive facts about an individual, such as medical conditions, business connections or political affiliations.
A common privacy scheme is to use a variant (in contrast to fixed) identifiers which is called pseudonyms to prevent an adversary from tracking and re-identifying the vehicle drivers. However, it was shown in  and  that tracking messages of variant pseudonyms is practically possible with high accuracy using the spatial and temporal information included in the messages. Although vehicle tracking is crucial in privacy breaching, the adversary have to correlate these anonymous traces to individual to achieve a real privacy threat. There are several de-anonymization techniques proposed in literature such as [3, 4, 5, 6], but most of them assume that each vehicle uses a fixed pseudonym for all of its traces which facilitates the de-anonymization process. In this thesis, you will work on the variant pseudonyms case to evaluate how effective the de-anonymization can be attained.
The following aspects should be studied during the thesis, but not limited to:
- Investigate how vehicle traces with variant pseudonyms can be returned back and grouped to their originating vehicle (Clustering).
- Evaluate and enhance the clustering technique when vehicle traces are fragmented, noised, incomplete, etc. (Data cleaning/enhancement).
- Develop an de-anonymization technique similar to ones used in references [3, 4, 5, 6].
- Of course, you need, at first, to obtain (or generate) realistic vehicle traces that reflect navigation among true drivers activities and point of interests (e.g. home, work, shopping mall, etc.) (search for: crawdad traces).
- Proven practical skills in Data Mining and/or Machine Learning
- Good programming skills using Java, C++ or Matlab
- Overall grade: at least 2.0
- Self-motivated, Innovative and Independent
If you are interested and really motivated, send your CV and transcript to me on emara(at)in.tum.de
 B. Wiedersheim, Z. Ma, F. Kargl, and P. Papadimitratos, “Privacy in inter-vehicular networks: Why simple pseudonym change is not enough,” in Wireless On-demand Network Systems and Services (WONS), 2010 Seventh International Conference on, pp. 176 –183, Feb. 2010.
 K. Emara, W. Woerndl, and J. Schlichter, “Vehicle tracking using vehicular network beacons,” in Fourth International Workshop on Data Security and PrivAcy in wireless Networks 2013 (D-SPAN 2013), (Madrid, Spain), June 2013.
 B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady, “Enhancing Security and Privacy in Traffic-Monitoring Systems,” Pervasive Computing, IEEE, vol. 5, no. 4, pp. 38–46, 2006.
 P. Golle and K. Partridge, “On the anonymity of home/work location pairs,” Pervasive Computing, pp. 2–9, 2009.
 H. Zang and J. Bolot, “Anonymization of location data does not work: A large-scale measurement study,” in Proceedings of the 17th annual international conference on Mobile computing and networking, pp. 145–156, 2011.
 S. Gambs, M.-O. Killijian, and M. N. D. P. Cortez, “De-anonymization attack on geolocated data,” 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 789–797, July 2013.