Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems
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 concern...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-03-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/16/3/1586 |
| _version_ | 1811279070281859072 |
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| author | Javier Parra-Arnau David Rebollo-Monedero Jordi Forné |
| author_facet | Javier Parra-Arnau David Rebollo-Monedero Jordi Forné |
| author_sort | Javier Parra-Arnau |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:48:09Z |
| format | Article |
| id | doaj.art-cd770c616ee044afa56a86b73c1a633c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T00:48:09Z |
| publishDate | 2014-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-cd770c616ee044afa56a86b73c1a633c2022-12-22T03:09:57ZengMDPI AGEntropy1099-43002014-03-011631586163110.3390/e16031586e16031586Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation SystemsJavier Parra-Arnau0David Rebollo-Monedero1Jordi Forné2Department of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), C. Jordi Girona 1-3, Barcelona 08034, SpainDepartment of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), C. Jordi Girona 1-3, Barcelona 08034, SpainDepartment of Telematics Engineering, Universitat Politècnica de Catalunya (UPC), C. Jordi Girona 1-3, Barcelona 08034, SpainRecommendation 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.http://www.mdpi.com/1099-4300/16/3/1586information privacyKullback-Leibler divergenceShannon’s entropyuser profilingprivacy-enhancing technologiesdata perturbationrecommendation systems |
| spellingShingle | Javier Parra-Arnau David Rebollo-Monedero Jordi Forné Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems Entropy information privacy Kullback-Leibler divergence Shannon’s entropy user profiling privacy-enhancing technologies data perturbation recommendation systems |
| title | Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems |
| title_full | Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems |
| title_fullStr | Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems |
| title_full_unstemmed | Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems |
| title_short | Optimal Forgery and Suppression of Ratings for Privacy Enhancement in Recommendation Systems |
| title_sort | optimal forgery and suppression of ratings for privacy enhancement in recommendation systems |
| topic | information privacy Kullback-Leibler divergence Shannon’s entropy user profiling privacy-enhancing technologies data perturbation recommendation systems |
| url | http://www.mdpi.com/1099-4300/16/3/1586 |
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