Privacy Preservation Improvement by Learning Optimal Profile Generation Rate

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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.