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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.
ISSN: 09574174
Other Identifiers: 10.1016/j.eswa.2016.11.018
Appears in Collections:Department of Applied Informatics

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