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Privacy Preservation Improvement by Learning Optimal Profile Generation Rate

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User Modeling 2003 (UM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2702))

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Abstract

PRAW, a privacy model proposed recently, is aimed at protecting Web surfers’ privacy by hiding their interests, i.e., their profiles. PRAW generates several faked transactions for each real user’s transaction. The faked transactions relate to various fields of interest in order to confuse eavesdroppers attempting to derive users’ profiles. They provide eavesdroppers with inconsistent data for the profile generation task. PRAW creates two profiles, a real user profile and a faked one aimed at confusing eavesdroppers. In this paper we demonstrate that the number of user transactions used for user profile generation significantly affects PRAW’s ability to hide users’ interests. We claim that there exists an optimal profile update rate for every user according to his surfing behavior. A system implementing PRAW needs to learn, for each specific user, the user’s behavior, and dynamically adjust the optimal number of transactions that should be used to generate the user profile.

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Author information

Authors and Affiliations

  1. Department of Information Systems Engineering, Ben-Gurion University, Beer-Sheva, Israel

    Tsvi Kuflik, Bracha Shapira, Yuval Elovici & Adlai Maschiach

  2. Department of Software Systems, Sapir College, Israel

    Tsvi Kuflik

Authors
  1. Tsvi Kuflik
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  2. Bracha Shapira
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  3. Yuval Elovici
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  4. Adlai Maschiach
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Editor information

Editors and Affiliations

  1. School of Information Sciences, Department of Information Science and Telecommunications, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, USA

    Peter Brusilovsky

  2. Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Albert Corbett

  3. Intelligent Interfaces, Department of Informatics, University of Bari, Via Orabona 4, 70126, Bari, Italy

    Fiorella de Rosis

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© 2003 Springer-Verlag Berlin Heidelberg

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Kuflik, T., Shapira, B., Elovici, Y., Maschiach, A. (2003). Privacy Preservation Improvement by Learning Optimal Profile Generation Rate. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds) User Modeling 2003. UM 2003. Lecture Notes in Computer Science(), vol 2702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44963-9_23

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  • DOI: https://doi.org/10.1007/3-540-44963-9_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40381-4

  • Online ISBN: 978-3-540-44963-8

  • eBook Packages: Springer Book Archive

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