Re-identification of Anonymized CDR datasets Using Social Network Data

In this work we examine a large dataset of 335 million anonymized call records made by 3 million users during 47 days in a region of northern Italy. Combining this dataset with publicly available user data, from different social networking ser-vices, we present a probabilistic approach to evaluate the potential of re-identification of the anonymized call records dataset. In this sense, our work explores different ways of analyzing data and data fusion techniques to integrate different mobility datasets together. On the one hand, this kind of approaches can breach users' privacy despite anonymization, so it is worth studying carefully. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information form multiple sources can complement and enrich each other.