Privacy-Preserving Location Publishing under Road-Network Constraints

  • Dan Lin
  • Sashi Gurung
  • Wei Jiang
  • Ali Hurson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


We are experiencing the expanding use of location-based services such as AT&T TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which have 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 paper, we first identify a new privacy problem, namely inference route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have shown that our approach outperforms the latest related work in terms of both efficiency and effectiveness.


Road Segment Edit Distance Average Error Rate Candidate Cluster Anonymization Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dan Lin
    • 1
  • Sashi Gurung
    • 1
  • Wei Jiang
    • 1
  • Ali Hurson
    • 1
  1. 1.Missouri University of Science and Technology 

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