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Measuring the privacy of user profiles in personalized information systems
Parra Arnau, Javier; Rebollo Monedero, David; Forné Muñoz, Jorge
Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica; Universitat Politècnica de Catalunya. SERTEL - Serveis Telemàtics
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
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
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
Article
Elsevier
         

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