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Privacy preserving for tagging recommender systems
conference contribution
posted on 2013-01-01, 00:00 authored by Tianqing Zhu, Gang LiGang Li, Yongli Ren, Wanlei Zhou, P XiongTagging 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.
History
Event
IEEE/WIC/ACM International Conference on Web Intelligence (2013 : Atlanta, Georgia)Pagination
81 - 88Publisher
IEEELocation
Atlanta, GeorgiaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-11-17End date
2013-11-20ISBN-13
9781479929023Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
2013, IEEEEditor/Contributor(s)
V Raghavan, X Hu, C Liau, J TreurTitle of proceedings
WI-IAT 2013 : Proceedings of the IEEE/WIC/ACM International Conference on Web IntelligenceUsage metrics
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