Where Were You Yesterday: Privacy Risk of Published Anonymous Trajectories
Shan Chang, Xiaoqiang Liu, Hongzi Zhu, Mianxiong Dong, Kaoru Ota, Ting Lu · 2016
With more and more trajectory traces available, conducting analysis and mining on those trajectories can obtain valuable information. Although the published traces are often made anonymous by substituting the true identities of mobile nodes with random identifiers, the privacy concern remains. In this paper, we propose a new de-anonymization attack based on the movement pattern of moving objects. Since moving objects are open to observe in public spaces, an attacker can easily learn information about a victim's movement either through direct observations or from third parties. After collecting a few trajectory segments of a mobile object, the movement pattern of the victim can be extracted, using an improved TF-IDF method. By comparing the movement pattern of the victim with those extracted from historical anonymous traces, it is possible to identify the victim from the anonymous traces. We conduct extensive trace-driven simulations and the results demonstrate that the attacker is able to de-anonymize anonymous trajectories with high probability.