Identifying unsafe routes for network-based trajectory privacy

Aris Gkoulalas-Divanis, Vassilios S. Verykios, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

In this paper, we propose a privacy model that offers trajectory privacy to the requesters of Location-Based Services (LBSs), by utilizing an underlying network of user movement. The privacy model has been implemented as a framework that (i) reconstructs the user movement from a series of independent location updates, (ii) identifies routes where user privacy is at risk, and (iii) anonymizes online user requests for LBSs to protect the requester for as long as the service withstands completion. In order to achieve (iii), we propose two anonymization techniques, the K-present (weak) and the K-frequent (strong) Uajectory anonymity, and a second chance approach that takes over when anonymization fails to ensure that the privacy of the user is preserved. To the best of our knowledge, this is the first work to propose a trajectory privacy model that utilizes an underlying network of user movement to offer in an interactive way personalized privacy to online user requests on trajectory data.

Original languageEnglish
Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
Pages937-948
Number of pages12
Volume2
Publication statusPublished - 31 Dec 2009
Externally publishedYes
Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
Duration: 30 Apr 20092 May 2009

Other

Other9th SIAM International Conference on Data Mining 2009, SDM 2009
CountryUnited States
CitySparks, NV
Period30/4/092/5/09

    Fingerprint

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics

Cite this

Gkoulalas-Divanis, A., Verykios, V. S., & Mokbel, M. (2009). Identifying unsafe routes for network-based trajectory privacy. In Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 (Vol. 2, pp. 937-948)