Linking anonymous location traces through driving characteristics

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.