Preserving User Location Privacy in Mobile Data Management Infrastructures

  • Reynold Cheng
  • Yu Zhang
  • Elisa Bertino
  • Sunil Prabhakar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4258)


Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user’s location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.


Service Provider Global Position System Range Query Location Privacy Uncertainty Region 
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 2006

Authors and Affiliations

  • Reynold Cheng
    • 1
  • Yu Zhang
    • 2
  • Elisa Bertino
    • 2
  • Sunil Prabhakar
    • 2
  1. 1.The Hong Kong Polytechnic UniversityHung HomHong Kong
  2. 2.Purdue UniversityWest LafayetteUSA

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