Next Article in Journal
The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration
Previous Article in Journal
Increasing the Discriminatory Power of DEA Using Shannon’s Entropy
Article Menu

Export Article

Open AccessArticle
Entropy 2014, 16(3), 1586-1631; doi:10.3390/e16031586

Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems

Department of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), C. Jordi Girona 1-3, Barcelona 08034, Spain
*
Author to whom correspondence should be addressed.
Received: 9 January 2014 / Revised: 7 February 2014 / Accepted: 12 March 2014 / Published: 21 March 2014
View Full-Text   |   Download PDF [1510 KB, uploaded 24 February 2015]   |  

Abstract

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 the combination of two perturbative mechanisms—the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation’s accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of such trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user’s and the population’s item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, an optimization problem that includes, as a particular case, the maximization of the entropy of the perturbed profile. We characterize the optimal trade-off surface among privacy, forgery rate and suppression rate,and experimentally evaluate how our approach could contribute to privacy protection in a real-world recommendation system. View Full-Text
Keywords: information privacy; Kullback-Leibler divergence; Shannon’s entropy; user profiling; privacy-enhancing technologies; data perturbation; recommendation systems information privacy; Kullback-Leibler divergence; Shannon’s entropy; user profiling; privacy-enhancing technologies; data perturbation; recommendation systems
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Parra-Arnau, J.; Rebollo-Monedero, D.; Forné, J. Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems. Entropy 2014, 16, 1586-1631.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top