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Differential privacy for neighborhood-based collaborative filtering
conference contribution
posted on 2013-01-01, 00:00 authored by Tianqing Zhu, Gang LiGang Li, Yongli Ren, Wanlei Zhou, P XiongAs 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.
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Event
Advances in Social Networks Analysis and Mining. IEEE/ACM International Conference (2013 : Niagara Falls, Ontario)Pagination
752 - 759Publisher
IEEELocation
Niagara, OntarioPlace of publication
Piscataway, NJ, USAStart date
2013-08-25End date
2013-08-28ISBN-13
9781479914968Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEE/ACMEditor/Contributor(s)
T Ozyer, P Carrington, E LimTitle of proceedings
ASONAM 2013 : Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningUsage metrics
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