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Position transformation: a location privacy protection method for moving objects
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Geographic Information Systems archive
Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS table of contents
Irvine, California
SESSION: Location privacy table of contents
Pages 62-71  
Year of Publication: 2008
ISBN:978-1-60558-324-2
Authors
Dan Lin  Missouri University of Science & Technology
Elisa Bertino  Purdue University
Reynold Cheng  Hong Kong University
Sunil Prabhakar  Purdue University
Sponsors
SIGSPATIAL : ACM Special Interest Group on Spatial Information
CERIAS : The Center for Education and Research in Information Assurance and Security
OCR : IBM Open Collaboartive Research Initiative
Publisher
ACM  New York, NY, USA
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ABSTRACT

The expanding use of location-based services has profound implications on the privacy of personal information. In this paper, we propose a framework for preserving location privacy based on the idea of sending to the service provider suitably modified location information. 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. We also define a privacy model to analyze our framework, and examine our approach experimentally.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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R. Cheng, Y. Zhang, E. Bertino, and S. Prabhakar. Preserving user location privacy in mobile data management infrastructures. In Proc. Workshop on Privacy Enhancing Technologies, 2006.
 
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B. Gedik and L. Liu. A customizable k-anonymity model for protecting location privacy. In Proc. IEEE ICDCS, pages 620--629, 2005.
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A. Khoshgozaran and C. Shahabi. Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In Proc. SSTD, pages 239--257, 2007.
 
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M. L. Yiu, C. S. Jensen, X. Huang, and H. Lu. A random rotation perturbation approach to privacy preserving data classification. In Proc. ICDE, 2008.


Collaborative Colleagues:
Dan Lin: colleagues
Elisa Bertino: colleagues
Reynold Cheng: colleagues
Sunil Prabhakar: colleagues