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Abstract
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.
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Authors and Affiliations
The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Reynold Cheng
Purdue University, West Lafayette, IN, 47907-1398, USA
Yu Zhang, Elisa Bertino & Sunil Prabhakar
- Reynold Cheng
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- Yu Zhang
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- Elisa Bertino
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- Sunil Prabhakar
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Editors and Affiliations
Microsoft Research, Cambridge, UK
George Danezis
Palo Alto Research Center, 3333 Coyote Hill Rd, 94304, Palo Alto, CA, USA
Philippe Golle
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© 2006 Springer-Verlag Berlin Heidelberg
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Cheng, R., Zhang, Y., Bertino, E., Prabhakar, S. (2006). Preserving User Location Privacy in Mobile Data Management Infrastructures. In: Danezis, G., Golle, P. (eds) Privacy Enhancing Technologies. PET 2006. Lecture Notes in Computer Science, vol 4258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957454_23
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