Location Privacy in Moving-Object Environments

  • Authors:

  • Dan Lin;Elisa Bertino;Reynold Cheng;Sunil Prabhakar

  • Affiliations:

  • Department of Computer Science/ Missouri University of Science and Technology/ 500 w. 15th St., Rolla, MO 65409, USA. e-mail: lindan@mst.edu;Department of Computer Science/ Purdue University/ 305 N. University St./ West Lafayette, IN 47907, USA. e-mail: bertino@cs.purdue.edu;Department of Computer Science/ Hong Kong University/ Pokfulam Road/ Hong Kong. e-mail: ckcheng@cs.hku.hk;Department of Computer Science/ Purdue University/ 305 N. University St./ West Lafayette, IN 47907, USA. e-mail: sunil@cs.purdue.edu

  • Venue:
  • Transactions on Data Privacy
  • Year:
  • 2009

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Abstract

The expanding use of location-based services has profound implications on the privacy of personal information. If no adequate protection is adopted, information about movements of specific individuals could be disclosed to unauthorized subjects or organizations, thus resulting in privacy breaches. In this paper, we propose a framework for preserving location privacy in moving-object environments. Our approach is based on the idea of sending to the service provider suitably modified location information. Such modifications, that include transformations like scaling, are performed by agents interposed between users and service providers. Agents execute data transformation and the service provider directly processes the transformed dataset. Our technique not only prevents the service provider from knowing the exact locations of users, but also protects information about user movements and locations from being disclosed to other users who are not authorized to access this information. A key characteristic of our approach is that it achieves privacy without degrading service quality. We also define a privacy model to analyze our framework, and examine our approach experimentally.