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Measuring the privacy of user profiles in personalized information systems

Autor
Parra-Arnau, J.; Rebollo-Monedero, D.; Forne, J.
Tipus d'activitat
Article en revista
Revista
Future generation computer systems
Data de publicació
2014-04-01
Volum
33
Pàgina inicial
53
Pàgina final
63
DOI
https://doi.org/10.1016/j.future.2013.01.001 Obrir en finestra nova
Projecte finançador
Ares - Team for Advanced REsearch on information Security and privacy
Ares. team for Advanced REsearch on information Security and privacy
GRUP SEGURETAT DE LA INFORMACIÓ (ISG)
Privacidad y QOS para sistemas de transporte
Repositori
http://hdl.handle.net/2117/22514 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0167739X1300006X Obrir en finestra nova
Resum
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 wor...
Citació
Parra-Arnau, J.; Rebollo-Monedero, D.; Forne, J. Measuring the privacy of user profiles in personalized information systems. "Future generation computer systems", 01 Abril 2014, vol. 33, p. 53-63.
Paraules clau
Kullback-Leibler divergence, Model, Personalized information systems, Privacy criterion, Privacy-enhancing technologies, Query forgery, Retrieval, Shannon's entropy, T-Closeness, User profiling, Web
Grup de recerca
SISCOM - Smart Services for Information Systems and Communication Networks

Participants