Document Type
Journal Article
Department/Unit
Department of Computer Science
Title
Privacy-preserving trajectory stream publishing
Language
English
Abstract
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.
Keywords
Data mining, Data sharing, Data stream, Privacy protection, Spatio-temporal databases
Publication Date
11-2014
Source Publication Title
Data and Knowledge Engineering
Volume
94
Issue
Part A
Start Page
89
End Page
109
Publisher
Elsevier
Peer Reviewed
1
Funder
The research is supported in part by the Discovery Grants (356065-2013) from the Natural Sciences and Engineering Research Council of Canada (NSERC).
DOI
10.1016/j.datak.2014.09.004
Link to Publisher's Edition
ISSN (print)
0169023X
ISSN (electronic)
18726933
Recommended Citation
Al-Hussaeni, Khalil, Benjamin C.M. Fung, and William K. Cheung. "Privacy-preserving trajectory stream publishing." Data and Knowledge Engineering 94.Part A (2014): 89-109.