Privacy-preserving topic model for tagging recommender systems

Zhu, Tianqing, Li, Gang, Zhou, Wanlei, Xiong, Ping and Yuan, Cao 2016, Privacy-preserving topic model for tagging recommender systems, Knowledge and information systems, vol. 46, no. 1, pp. 33-58, doi: 10.1007/s10115-015-0832-9.

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Title Privacy-preserving topic model for tagging recommender systems
Author(s) Zhu, TianqingORCID iD for Zhu, Tianqing
Li, GangORCID iD for Li, Gang
Zhou, WanleiORCID iD for Zhou, Wanlei
Xiong, Ping
Yuan, Cao
Journal name Knowledge and information systems
Volume number 46
Issue number 1
Start page 33
End page 58
Total pages 26
Publisher Springer
Place of publication Berlin, Gemany
Publication date 2016-01
ISSN 0219-1377
Keyword(s) privacy preserving
differential privacy
topic model
recommender system
tagging system
Summary Tagging recommender systems provide users the freedom to explore tags and obtain recommendations. The releasing and sharing of these tagging datasets will accelerate both commercial and research work on recommender systems. However, releasing the original tagging datasets is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from tagging datasets with only a little background information. Recently, several privacy techniques have been proposed to address the problem, but most of these lack a strict privacy notion, and rarely prevent individuals being re-identified from the dataset. This paper proposes a privacy- preserving tag release algorithm, PriTop. This algorithm is designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset. The proposed PriTop algorithm includes three privacy-preserving operations: Private topic model generation structures the uncontrolled tags; private weight perturbation adds Laplace noise into the weights to hide the numbers of tags; while private tag selection finally finds the most suitable replacement tags for the original tags, so the exact tags can be hidden. We present extensive experimental results on four real-world datasets, Delicious, MovieLens, and BibSonomy. While the recommendation algorithm is successful in all the cases, our results further suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy.
Language eng
DOI 10.1007/s10115-015-0832-9
Field of Research 080109 Pattern Recognition and Data Mining
0801 Artificial Intelligence And Image Processing
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 ©2016, Springer
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Document type: Journal Article
Collection: School of Information Technology
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