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Privacy preserving data release for tagging recommender systems

Zhu, Tianqing, Li, Gang, Ren, Yongli, Zhou, Wanlei and Xiong, Ping 2015, Privacy preserving data release for tagging recommender systems, Web intelligence, vol. 13, no. 4, pp. 229-246, doi: 10.3233/WEB-150323.

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Title Privacy preserving data release for tagging recommender systems
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
Ren, Yongli
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Xiong, Ping
Journal name Web intelligence
Volume number 13
Issue number 4
Start page 229
End page 246
Total pages 18
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2015
ISSN 2405-6456
2405-6464
Keyword(s) privacy preserving
differential privacy
recommender system
tagging
Summary Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, 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, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive 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 Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity.
Language eng
DOI 10.3233/WEB-150323
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IOS Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082000

Document type: Journal Article
Collection: School of Information Technology
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