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.

Attached Files
Name Description MIMEType Size Downloads

Title Privacy preserving collaborative filtering for KNN attack resisting
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
Pan, LeiORCID iD for Pan, Lei orcid.org/0000-0002-4691-8330
Ren, Yongli
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
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
1869-5469
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077816

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 252 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 09 May 2016, 15:43:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.