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Deferentially private tagging recommendation based on topic model
conference contribution
posted on 2014-01-01, 00:00 authored by Tianqing Zhu, Gang LiGang Li, Wanlei Zhou, Ping Xiong, C YuanTagging recommender system allows Internet users to annotate resources with personalized tags and provides users the freedom to obtain recommendations. However, It is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive tags with only a little background information. This paper proposes a privacy preserving tagging release algorithm, PriTop, which is designed to protect users under the notion of differential privacy. 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. We present extensive experimental results on four real world datasets and results suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy. © 2014 Springer International Publishing.
History
Volume
8443 LNAIIssue
PART 1Pagination
557 - 568Publisher DOI
ISSN
0302-9743eISSN
1611-3349Publication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
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