Privacy preserving for tagging recommender systems

Zhu, Tianqing, Li, Gang, Ren, Yongli, Zhou, Wanlei and Xiong, Ping 2013, Privacy preserving for tagging recommender systems, in WI-IAT 2013 : Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, IEEE, Piscataway, N.J., pp. 81-88, doi: 10.1109/WI-IAT.2013.12.

Attached Files
Name Description MIMEType Size Downloads

Title Privacy preserving for tagging recommender systems
Author(s) Zhu, TianqingORCID iD for Zhu, Tianqing
Li, GangORCID iD for Li, Gang
Ren, Yongli
Zhou, WanleiORCID iD for Zhou, Wanlei
Xiong, Ping
Conference name IEEE/WIC/ACM International Conference on Web Intelligence (2013 : Atlanta, Georgia)
Conference location Atlanta, Georgia
Conference dates 17-20 Nov. 2013
Title of proceedings WI-IAT 2013 : Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Editor(s) Raghavan, Vijay
Hu, Xiaolin
Liau, Churn-Jung
Treur, Jan
Publication date 2013
Conference series IEEE/WIC/ACM International Conference on Web Intelligence
Start page 81
End page 88
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Differential privacy
Privacy preserving
Tagging recommender system
Summary Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.
ISBN 9781479929023
Language eng
DOI 10.1109/WI-IAT.2013.12
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
ERA Research output type E Conference publication
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL

Document type: Conference Paper
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.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 330 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Tue, 18 Mar 2014, 08:32:41 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