Name
Abstract | ||
|---|---|---|
This article proposes a new technique for Privacy Preserving Collaborative Filtering (PPCF) based on microaggregation, which provides accurate recommendations estimated from perturbed data whilst guaranteeing user k-anonymity. The experimental results presented in this article show the effectiveness of the proposed technique in protecting users' privacy without compromising the quality of the recommendations. In this sense, the proposed approach perturbs data in a much more efficient way than other well-known methods such as Gaussian Noise Addition (GNA). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.jcss.2014.12.013 | J. Comput. Syst. Sci. |
Keywords | Field | DocType |
recommender systems,electronic commerce | Recommender system,Data mining,Collaborative filtering,Computer science,Gaussian noise,Privacy software | Journal |
Volume | Issue | ISSN |
81 | 6 | 0022-0000 |
Citations | PageRank | References |
24 | 0.72 | 31 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fran Casino | 1 | 94 | 13.51 |
| Josep Domingo-Ferrer | 2 | 3231 | 404.42 |
| Constantinos Patsakis | 3 | 325 | 41.68 |
| Domènec Puig | 4 | 84 | 7.98 |
| Agusti Solanas | 5 | 687 | 50.73 |