Why you go reveals who you know: Disclosing social relationship by cooccurrence

Abstract

The popularity of location-based services (LBS) and the ubiquity of sensor device have resulted in rich spatiotemporal data. A large number of human behaviors had been recorded including cooccurrence which refers to the phenomenon that two people have been to the same places at the same time. These data enable attackers to infer people's social relationship based on their cooccurrences and many attack models were proposed. However, current attack models still cannot effectively address the following two challenges: How to distinguish cooccurrences between acquaintances and strangers? What kind of cooccurrence contributes to strong social strength? In this paper, we present a novel social relationship attack model - the Mobility Intention-based Relationship Inference (MIRI) model - which can solve the above two issues. Firstly, we extract mobility intentions and adopt them to characterize cooccurrences. A classification model is trained for attacking social relationship. The experimental results on two real-world datasets demonstrate that the proposed MIRI model can properly differentiate cooccurrences by simultaneously considering spatial and temporal features. The comparison results also indicate that MIRI model significantly outperforms state-of-the-art social relationship attack models.