Unique on the road: Re-identification of vehicular location-based metadata

Abstract

For digging individuals’ information from anonymous metadata, usually the first step is to identify the entities in metadata and associate them with persons in the real world. If an entity in metadata is uniquely re-identified, its host is possibly confronting a serious privacy disclosure problem. In this paper, we study the privacy issue in VLBS (Vehicular Location-Based Service) by investigating the re-identification problem of vehicular location-based metadata in a VLBS server. We find that the trajectories of vehicles are highly unique after studying 131 millions mobility traces of taxis in Shenzhen and 1.1 billions of taxis in Shanghai. More specifically, with the help of the urban road maps, four spatio-temporal points are sufficient to uniquely identify vehicles, achieving an accuracy of 95.35%. This indicates that there is a high risk of privacy leakage when VLBS applications are widely deployed.