Privacy preserving collaborative filtering for KNN attack resisting

Zhu, Tianqing, Li, Gang, Pan, Lei, Ren, Yongli and Zhou, Wanlei 2014, Privacy preserving collaborative filtering for KNN attack resisting, Social network analysis and mining, vol. 4, Article Number : 196, pp. 1-14, doi: 10.1007/s13278-014-0196-2.

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Title Privacy preserving collaborative filtering for KNN attack resisting
Author(s) Zhu, TianqingORCID iD for Zhu, Tianqing
Li, GangORCID iD for Li, Gang
Pan, LeiORCID iD for Pan, Lei
Ren, Yongli
Zhou, Wanlei
Journal name Social network analysis and mining
Volume number 4
Season Article Number : 196
Start page 1
End page 14
Total pages 14
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014-12
ISSN 1869-5450
Keyword(s) Privacy preserving
Neighborhood-based collaborative filtering
Differential privacy
Summary Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.
Language eng
DOI 10.1007/s13278-014-0196-2
Field of Research 080303 Computer System Security
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
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