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Sanitization of Call Detail Records via Differentially-Private Bloom Filters

  • Mohammad Alaggan
  • Sébastien  Gambs
  • Stan Matwin
  • Mohammed Tuhin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9149)

Abstract

Publishing directly human mobility data raises serious privacy issues due to its inference potential, such as the (re-)identification of individuals. To address these issues and to foster the development of such applications in a privacy-preserving manner, we propose in this paper a novel approach in which Call Detail Records (CDRs) are summarized under the form of a differentially-private Bloom filter for the purpose of privately estimating the number of mobile service users moving from one area (region) to another in a given time frame. Our sanitization method is both time and space efficient, and ensures differential privacy while solving the shortcomings of a solution recently proposed. We also report on experiments conducted using a real life CDRs dataset, which show that our method maintains a high utility while providing strong privacy.

Keywords

Hash Function Bloom Filter Mean Relative Error Differential Privacy Telecom Operator 
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.

Notes

Acknowledgments

This work was partially supported by the MSR-INRIA joint lab as well as the INRIA project lab CAPPRIS, and by NSERC Canada.

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Mohammad Alaggan
    • 1
  • Sébastien  Gambs
    • 2
  • Stan Matwin
    • 3
  • Mohammed Tuhin
    • 3
  1. 1.Helwan UniversityCairoEgypt
  2. 2.Université de Rennes 1 - InriaRennesFrance
  3. 3.Dalhousie UniversityHalifaxCanada

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