Presentation 2012-07-30
A Comparison of Clustering Methods in Privacy Preserving Collaborative Filtering
Yui MATSUMOTO, Katsuhiro HONDA, Akira NOTSU, Hidetomo ICHIHASHI,
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Abstract(in English) Collaborative filtering achieves personalized recommendation based on user collaboration. In this paper, how to preserve personal information in collaborative filtering is studied through several comparative experiments. k-anonymization is a standard method for guaranteeing personal privacy, in which data records are summarized so that any record is indistinguishable from at least (k-1) other records. This study compares several clustering-based k-anonymization models in the context of collaborative filtering application.
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Keyword(in English) Privacy Preserving / Collaborative Filtering / Clustering Methods
Paper # NC2012-14
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Committee NC
Conference Date 2012/7/23(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Comparison of Clustering Methods in Privacy Preserving Collaborative Filtering
Sub Title (in English)
Keyword(1) Privacy Preserving
Keyword(2) Collaborative Filtering
Keyword(3) Clustering Methods
1st Author's Name Yui MATSUMOTO
1st Author's Affiliation Graduate School of Engineering, Osaka Prefecture University()
2nd Author's Name Katsuhiro HONDA
2nd Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
3rd Author's Name Akira NOTSU
3rd Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
4th Author's Name Hidetomo ICHIHASHI
4th Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
Date 2012-07-30
Paper # NC2012-14
Volume (vol) vol.112
Number (no) 168
Page pp.pp.-
#Pages 4
Date of Issue