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oai:recercat.cat:2117/225142026-02-04T08:01:33Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Parra Arnau, Javier author Rebollo Monedero, David author Forné Muñoz, Jorge author 2014-04-01 Personalized information systems are information-filtering systems that endeavor to tailor information-exchange functionality to the specific interests of their users. The ability of these systems to profile users is, on the one hand, what enables such intelligent functionality, but on the other, the source of innumerable privacy risks. In this paper, we justify and interpret KL divergence as a criterion for quantifying the privacy of user profiles. Our criterion, which emerged from previous work in the domain of information retrieval, is here thoroughly examined by adopting the beautiful perspective of the method of types and large deviation theory, and under the assumption of two distinct adversary models. In particular, we first elaborate on the intimate connection between Jaynes' celebrated method of entropy maximization and the use of entropies and divergences as measures of privacy; and secondly, we interpret our privacy metric as false positives and negatives in a binary hypothesis testing. (C) 2013 Elsevier B.V. All rights reserved. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica Computer security Data protection Personalized information systems User profiling Privacy-enhancing technologies Privacy criterion Shannon's entropy Kullback-Leibler divergence Query forgery T-Closeness Web Retrieval Model Seguretat informàtica Protecció de dades Measuring the privacy of user profiles in personalized information systems