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Article
Traffic Information Publication with Privacy Preservation
ACM Transactions on Intelligent Systems and Technology
  • Sashi Gurung
  • Dan Lin, Missouri University of Science and Technology
  • Wei Jiang, Missouri University of Science and Technology
  • A. R. Hurson, Missouri University of Science and Technology
  • Rui Zhang
Abstract

We are experiencing the expanding use of location-based services such as AT&T’s TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which has great potential for statistical usage in applications like traffic flow analysis, infrastructure planning, and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this article, we first identify a new privacy problem, namely, the inference-route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.

Department(s)
Computer Science
Comments

Special Section on Urban Computing

Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2014 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
9-1-2014
Publication Date
01 Sep 2014
Disciplines
Citation Information
Sashi Gurung, Dan Lin, Wei Jiang, A. R. Hurson, et al.. "Traffic Information Publication with Privacy Preservation" ACM Transactions on Intelligent Systems and Technology Vol. 5 Iss. 3 (2014) ISSN: 2157-6904; 2157-6912
Available at: http://works.bepress.com/a-hurson/43/