Privacy-preserving trajectory stream publishing

摘要

Recent advancement in mobile computing and sensory technology has facilitated the possibility of continuously updating, monitoring, and detecting the latest location and status of moving individuals. Spatio-temporal data generated and collected on the fly are described as trajectory streams. This work is motivated by the concern that publishing individuals' trajectories on the fly may jeopardize their privacy. In this paper, we illustrate and formalize two types of privacy attacks against moving individuals. We devise a novel algorithm, called Incremental Trajectory Stream Anonymizer (ITSA), for incrementally anonymizing a sequence of sliding windows on trajectory stream. The sliding windows are dynamically updated with joining and leaving individuals. The sliding windows are updated by using an efficient data structure to accommodate massive volume of data. We conducted extensive experiments on simulated and real-life data sets to evaluate the performance of our method. Empirical results demonstrate that our method significantly lowers runtime compared to existing methods, and efficiently scales when handling massive data sets. To the best of our knowledge, this is the first work to anonymize high-dimensional trajectory stream.