Privacy Preservation Improvement by Learning Optimal Profile Generation Rate
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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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- Privacy Preservation Improvement by Learning Optimal Profile Generation Rate
- Book Title
- User Modeling 2003
- Book Subtitle
- 9th International Conference, UM 2003 Johnstown, PA, USA, June 22–26, 2003 Proceedings
- pp 168-177
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. School of Information Sciences, Department of Information Science and Telecommunications, University of Pittsburgh
- 2. Human Computer Interaction Institute, Carnegie Mellon University
- 3. Intelligent Interfaces, Department of Informatics, University of Bari
- Author Affiliations
- 6. Department of Information Systems Engineering, Ben-Gurion University, Beer-Sheva, Israel
- 7. Department of Software Systems, Sapir College, Israel
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