Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/295
Title: | Privacy-preserving collaborative recommendations based on random perturbations |
Authors: | Polatidis, Nikolaos Georgiadis, Christos K. Pimenidis, Elias Mouratidis, Haralambos |
Type: | Article |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | Collaborative filtering Random perturbations Multi-level privacy Recommender systems |
Issue Date: | 2017 |
Publisher: | Elsevier |
Source: | Expert Systems with Applications |
Volume: | 71 |
First Page: | 18 |
Last Page: | 25 |
Abstract: | Collaborative recommender systems offer a solution to the information overload problem found in online environments such as e-commerce. The use of collaborative filtering, the most widely used recommen- dation method, gives rise to potential privacy issues. In addition, the user ratings utilized in collaborative filtering systems to recommend products or services must be protected. The purpose of this research is to provide a solution to the privacy concerns of collaborative filtering users, while maintaining high accu- racy of recommendations. This paper proposes a multi-level privacy-preserving method for collaborative filtering systems by perturbing each rating before it is submitted to the server. The perturbation method is based on multiple levels and different ranges of random values for each level. Before the submission of each rating, the privacy level and the perturbation range are selected randomly from a fixed range of privacy levels. The proposed privacy method has been experimentally evaluated with the results showing that with a small decrease of utility, user privacy can be protected, while the proposed approach offers practical and effective results. |
URI: | https://doi.org/10.1016/j.eswa.2016.11.018 https://ruomo.lib.uom.gr/handle/7000/295 |
ISSN: | 09574174 |
Other Identifiers: | 10.1016/j.eswa.2016.11.018 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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Georgiadis_2017_Privacy-preserving collaborative.pdf | 866,16 kB | Adobe PDF | View/Open |
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