Differential privacy for neighborhood-based collaborative filtering

Zhu, Tianqing, Li, Gang, Ren, Yongli, Zhou, Wanlei and Xiong, Ping 2013, Differential privacy for neighborhood-based collaborative filtering, in ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, Piscataway, NJ, USA, pp. 752-759.

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Title Differential privacy for neighborhood-based collaborative filtering
Author(s) Zhu, TianqingORCID iD for Zhu, Tianqing orcid.org/0000-0003-3411-7947
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Ren, Yongli
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Xiong, Ping
Conference name Advances in Social Networks Analysis and Mining. IEEE/ACM International Conference (2013 : Niagara Falls, Ontario)
Conference location Niagara, Ontario
Conference dates 25-28 Aug. 2013
Title of proceedings ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Editor(s) Ozyer, Tansel
Carrington, Peter
Lim, Ee-Peng
Publication date 2013
Conference series IEEE/ACM International Conference Advances in Social Networks Analysis and Mining
Start page 752
End page 759
Total pages 8
Publisher IEEE
Place of publication Piscataway, NJ, USA
Keyword(s) privacy preserving
neighborhood-based collaborative filtering
differential privacy
Summary As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.
ISBN 9781479914968
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Copyright notice ©2013, IEEE/ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057137

Document type: Conference Paper
Collection: School of Information Technology
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