Deferentially private tagging recommendation based on topic model

Zhu,T, Li,G, Zhou,W, Xiong,P and Yuan,C 2014, Deferentially private tagging recommendation based on topic model. In Tseng,VS, Ho,TB, Zhou,ZH, Chen,ALP and Kao,HY (ed), Advances in Knowledge Discovery and Data Mining, Springer, Heidelberg, Germany, pp.557-568, doi: 10.1007/978-3-319-06608-0_46.

Attached Files
Name Description MIMEType Size Downloads

Title Deferentially private tagging recommendation based on topic model
Author(s) Zhu,TORCID iD for Zhu,T orcid.org/0000-0003-3411-7947
Li,GORCID iD for Li,G orcid.org/0000-0003-1583-641X
Zhou,WORCID iD for Zhou,W orcid.org/0000-0002-1680-2521
Xiong,P
Yuan,C
Title of book Advances in Knowledge Discovery and Data Mining
Editor(s) Tseng,VS
Ho,TB
Zhou,ZH
Chen,ALP
Kao,HY
Publication date 2014
Series Lecture Notes in Artificial Intelligence
Chapter number 46
Total chapters 50
Start page 557
End page 568
Total pages 12
Publisher Springer
Place of Publication Heidelberg, Germany
Keyword(s) Differential Privacy
Privacy Preserving
Recommendation
Tagging
Summary Tagging 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.
ISBN 9783319066080
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-06608-0_46
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071849

Document type: Book Chapter
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.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 513 Abstract Views, 8 File Downloads  -  Detailed Statistics
Created: Wed, 22 Apr 2015, 12:47:51 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 drosupport@deakin.edu.au.