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,T orcid.org/0000-0003-3411-7947 Li,G orcid.org/0000-0003-1583-641X Zhou,W 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 Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 27 Mar 2015, 15:19:01 EST Fri, 17 Apr 2015, 16:55:27 EST Wed, 22 Apr 2015, 12:47:51 EST Wed, 22 Apr 2015, 15:37:45 EST Wed, 22 Apr 2015, 15:49:30 EST Thu, 23 Apr 2015, 12:24:08 EST Thu, 23 Apr 2015, 12:30:35 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 1015 Abstract Views, 8 File Downloads  -  Detailed Statistics Created: Wed, 22 Apr 2015, 12:47:51 EST