A Method for Privacy-preserving Collaborative Filtering Recommendations

Georgiadis, Christos K., Polatidis, Nikolaos, Mouratidis, Haralambos and Pimenidis, Elias (2017) A Method for Privacy-preserving Collaborative Filtering Recommendations Journal of Universal Computer Science, 23 (2). pp. 146-166. ISSN 0948-695X

[img] Text
jucs_23_02_0146_0166_georgiadis.pdf - Published Version

Download (238kB)

Abstract

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) 'neighbourhood privacy' that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied.

Item Type: Journal article
Additional Information: © 2017 J.UCS. Journal of Universal Computer Science, vol. 23, no. 2 (2017), 146-166. http://www.jucs.org/jucs_23_2/a_method_for_privacy
Uncontrolled Keywords: Collaborative Filtering; Trust Network; Privacy; Recommender Systems
Subjects: G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
G000 Computing and Mathematical Sciences > G500 Information Systems
Depositing User: Converis
Date Deposited: 29 Sep 2017 03:01
Last Modified: 29 Sep 2017 08:15
URI: http://eprints.brighton.ac.uk/id/eprint/17441

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year