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International Conference on Privacy in Statistical Databases

PSD 2014: Privacy in Statistical Databases pp 312–325Cite as

Towards Secure and Practical Location Privacy through Private Equality Testing

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8744))

Abstract

In this paper, we propose a practical, privacy-preserving equality testing primitive which allows two users to learn if they share the same encrypted input data. Our protocol assumes no trust on a third party and/or other peers, and it is specifically suited for low-min entropy data (i.e., data that can be exhaustively searched by an attacker), such as encrypted users locations. We demonstrate that our primitive is secure and efficient: Two public-key exponentiations are required, per each user, for each equality testing. We give implementation results, showing that our primitive is practical in a multiple users scenario. Finally, we describe how we could use our primitive as a building block for a proximity testing buddy-finder service for social networks.

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Author information

Authors and Affiliations

  1. Department of Informatics, Ionian University, Plateia Tsirigoti 7, 49100, Kerkyra, Greece

    Emmanouil Magkos, Marios Magioladitis & Spyros Sioutas

  2. Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou, 18534, Piraeus, Greece

    Panayiotis Kotzanikolaou

  3. School of Science and Technology, Hellenic Open University, Parodos Aristotelous 18, GR-26335, Patras, Greece

    Vassilios S. Verykios

Authors
  1. Emmanouil Magkos
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  2. Panayiotis Kotzanikolaou
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  3. Marios Magioladitis
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  4. Spyros Sioutas
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  5. Vassilios S. Verykios
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Catalonia

    Josep Domingo-Ferrer

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Magkos, E., Kotzanikolaou, P., Magioladitis, M., Sioutas, S., Verykios, V.S. (2014). Towards Secure and Practical Location Privacy through Private Equality Testing. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-11257-2_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11256-5

  • Online ISBN: 978-3-319-11257-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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