Where You Are Is Who You Are: User Identification by Matching Statistics
Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. In particular, we focus on the setting in which we are given histograms of users' data collected during two different experiments. We assume that, in the first data set, the users' identities are anonymized or hidden and that, in the second data set, their identities are known. We study the task of identifying the users by matching the histograms of their data in the first data set with the histograms from the second data set. In recent works, the optimal algorithm for this user identification task is introduced. In this paper, we evaluate the effectiveness of this method on three different types of data sets with up to 50 000 users, and in multiple scenarios. Using data sets such as call data records, web browsing histories, and GPS trajectories, we demonstrate that a large fraction of users can be easily identified given only histograms of their data; hence, these histograms can act as users' fingerprints. We also verify that simultaneous identification of users achieves better performance compared with one-by-one user identification. Furthermore, we show that using the optimal method for identification indeed gives higher identification accuracy than the heuristics-based approaches in the practical scenarios. The accuracy obtained under this optimal method can thus be used to quantify the maximum level of user identification that is possible in such settings. We show that the key factors affecting the accuracy of the optimal identification algorithm are the duration of the data collection, the number of users in the anonymized data set, and the resolution of the data set. We also analyze the effectiveness of k-anonymization in resisting user identification attacks on these data sets.