A novel location privacy framework without trusted third party based on location anonymity prediction

  • Authors:

  • Sheikh Iqbal Ahamed;Md. Munirul Haque;Chowdhury Sharif Hasan

  • Affiliations:

  • Marquette University, Milwaukee, WI;Marquette University, Milwaukee, WI;University of California Irvine, Irvine, CA

  • Venue:
  • ACM SIGAPP Applied Computing Review
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Location-based services are becoming increasingly popular with the proliferation of location aware devices. It is not possible to access location-based services and preserve privacy at the same time when the user provides exact location information. Cloaking or obfuscating location data is the only way to protect location-privacy. To do that, most of the systems use third party location anonymizer. In this paper, we propose a novel location privacy framework without any trusted third party (TTP). Most of the existing solutions attempt to satisfy k-anonymity. However, there are several drawbacks of using fixed and user defined k. In order to solve the problems our proposed solution aims to meet probabilistic k-anonymity. Based on historic data expected number of users present in a place is predicted which is used as probabilistic anonymity level. Thus we eliminate the use of any TTP which results into improvement of query-processing time and provides fewer query results for the user to process eventually minimizing the overall response time. Users' exact location information is not revealed in either communication or computation process.