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An effective privacy preserving algorithm for neighborhood-based collaborative filtering

Zhu,T, Ren,Y, Zhou,W, Rong,J and Xiong,P 2014, An effective privacy preserving algorithm for neighborhood-based collaborative filtering, Future Generation Computer Systems, vol. 36, pp. 142-155, doi: 10.1016/j.future.2013.07.019.

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Title An effective privacy preserving algorithm for neighborhood-based collaborative filtering
Author(s) Zhu,TORCID iD for Zhu,T orcid.org/0000-0003-3411-7947
Ren,Y
Zhou,WORCID iD for Zhou,W orcid.org/0000-0002-1680-2521
Rong,J
Xiong,P
Journal name Future Generation Computer Systems
Volume number 36
Start page 142
End page 155
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014
ISSN 0167-739X
Keyword(s) Differential privacy
Neighborhood-based collaborative filtering
Privacy preserving
Summary As a popular technique in recommender systems, Collaborative Filtering (CF) has been the focus of significant attention in recent years, however, its privacy-related issues, especially for the neighborhood-based CF methods, cannot be overlooked. The aim of this study is to address these privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. This algorithm includes two privacy preserving operations: Private Neighbor Selection and Perturbation. Using the item-based method as an example, Private Neighbor Selection is constructed on the basis of the notion of differential privacy, meaning that neighbors are privately selected for the target item according to its similarities with others. Recommendation-Aware Sensitivity and a re-designed differential privacy mechanism are introduced in this operation to enhance the performance of recommendations. A Perturbation operation then hides the true ratings of selected neighbors by adding Laplace noise. The PNCF algorithm reduces the magnitude of the noise introduced from the traditional differential privacy mechanism. Moreover, a theoretical analysis is provided to show that the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations. The results from experiments on two real datasets show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss. © 2013 Published by Elsevier B.V. All rights reserved.
Language eng
DOI 10.1016/j.future.2013.07.019
Field of Research 080502 Mobile Technologies
080503 Networking and Communications
080504 Ubiquitous Computing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072459

Document type: Journal Article
Collection: School of Information Technology
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