Mobile user verification/identification using statistical mobility profile

Recent studies show that ubiquitous smartphone data, e.g., the universal cell tower IDs, WiFi access points, etc., can be used to effectively recover individuals' mobility. However, recording and releasing the data containing such information without anonymization can hurt individuals' location privacy. Therefore, many anonymization methods have been used to sanitize these datasets before they are shared to the research community. In this paper, we demonstrate the idea of statistical mobile user profiling and identification based on anonymized datasets. Our insight is that, the mobility patterns inferred from different individuals' data are identifiable by using the statistical profiles constructed from the patterns. Experimental results show that, the proposed method achieves a promising identification accuracy of 96% on average based on randomly chosen two users' data, which makes our framework feasible for the application of inferring the fraud usage of the smartphones. Also, extensive experiments are conducted on the more challenging cases, showing a 59.5% identification accuracy for a total of 50 users based on 636 weekly data segments and a 56.1% accuracy for a total of 63 users based on 786 weekly data segments for two separate datasets. As the first work of such kind, our result suggests good possibility of developing location-based services or applications on the ubiquitous location anonymized datasets.