Asynchronous side information attack from the edge: an approach to identify participants from anonymous mobility traces

With the increasing adoption of location-based social network applications, a large number of location traces of human mobility have been collected and published for the purpose of assisting mobile system design and scientific research. Most mobility traces are processed to achieve anonymity before publishing by the way of replacing the true IDs and introducing noise interference. In this paper, we show that such anonymous mobility traces are vulnerable to asynchronous side information attack from the edge: if partial movement information is exposed to some compromised edge nodes even after the data collection period, the adversary is able to identify the participant from the anonymous mobility traces with high probability. Our method to identify participants is based on exploring the accumulative temporal and spatial characteristics of individual movement. We introduce $$\delta$$δ-partition to divide user locations into sub-areas, and $$\epsilon$$ϵ-partition to group user activities into time intervals. We illustrate that a mobility trace can be uniquely represented by a set of frequent locations together with their active time intervals. We further derive a similarity measurement to be used by the adversary for asynchronous side information attack. We develop theoretical analysis to prove that an anonymous participant can be correctly identified with high probability under certain condition. Extensive experiments are conducted on three typical mobility datasets corresponding to the movement of bus, taxi and human, which show that the identification success ratio achieves 99%, 45% and 72%, respectively.