NDLI: Privacy preserving collaborative filtering for KNN attack resisting

Content Provider	SpringerLink
Author	Zhu, Tianqing Li, Gang Pan, Lei Ren, Yongli Zhou, Wanlei
Copyright Year	2014
Abstract	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.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	4
Issue Number	1
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2014-06-01
Publisher Place	Vienna
Access Restriction	Subscribed
Subject Keyword	Privacy preserving Neighborhood-based collaborative filtering Differential privacy Data Mining and Knowledge Discovery Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Media Technology Human-Computer Interaction Information Systems Communication

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