Location Privacy Protection in the Presence of Users’ Preferences

  • Weiwei Ni
  • Jinwang Zheng
  • Zhihong Chong
  • Shan Lu
  • Lei Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)


Location privacy receives considerable attentions in emerging location based services. Most current practice however fails to incorporate users’ preferences. In this paper, we propose a privacy protection solution to allow users’ preferences in the fundamental query of k nearest neighbors. Particularly, users are permitted to choose privacy preferences by specifying minimum inferred region. By leveraging Hilbert curve based transformation, the additional workload from users’ preferences is alleviated. What’s more, this transformation reduces time-expensive region queries in two dimensional space to range ones in one dimensional space. Therefore, the time efficiency, as well as communication efficiency, is greatly improved due to clustering properties of Hilbert curve. The empirical studies demonstrate our implementation delivers both flexibility for users’ preferences and scalability for time and communication costs.


Range Query Privacy Protection Server Side Initial Region Location Privacy 
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 2011

Authors and Affiliations

  • Weiwei Ni
    • 1
  • Jinwang Zheng
    • 1
  • Zhihong Chong
    • 1
  • Shan Lu
    • 1
  • Lei Hu
    • 1
  1. 1.Southeast UniversityNanjingChina

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