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Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems

Autor
Parra-Arnau, J.; Rebollo-Monedero, D.; Forne, J.
Tipus d'activitat
Article en revista
Revista
Entropy: international and interdisciplinary journal of entropy and information studies
Data de publicació
2014-03
Volum
16
Número
3
Pàgina inicial
1586
Pàgina final
1631
DOI
https://doi.org/10.3390/e16031586 Obrir en finestra nova
Projecte finançador
CONSEQUENCE
Repositori
http://hdl.handle.net/2117/22511 Obrir en finestra nova
URL
http://www.mdpi.com/1099-4300/16/3/1586 Obrir en finestra nova
Resum
Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on t...
Citació
Parra-Arnau, J.; Rebollo-Monedero, D.; Forne, J. Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems. "Entropy: international and interdisciplinary journal of entropy and information studies", Març 2014, vol. 16, núm. 3, p. 1586-1631.
Paraules clau
Data perturbation, Information privacy, Kullback-Leibler divergence, Privacy-enhancing technologies, Recommendation systems, Shannon’s entropy, User profiling
Grup de recerca
SISCOM - Smart Services for Information Systems and Communication Networks

Participants