Presentation 2011-03-28
Privacy-preserving mining of frequent closed patterns using ZDDs
Keisuke OOTAKI, Akihiro YAMAMOTO,
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Abstract(in English) In this paper, we propose new methods for privacy-preserving mining of frequent closed patterns from horizontally partitioned distributed databases. Privacy-preserving data mining is to discover knowledge from large scale data that include private information and are stored in distributed places without revealing one's private information against for other participants. Assuming that distributed databases are horizontally partitioned, we treat closed pattern minining and apply the fast family algebra using ZDDs to privacy-preserving data mining. We propose two solutions for the problem that arise from using ZDDs for distributed data and estimate them with the semi-honest model.
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Keyword(in English) Closed pattern / Distributed database / Privacy-preserving data mining / Zero-suppressed BDD
Paper # IBISML2010-111
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Committee IBISML
Conference Date 2011/3/21(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Privacy-preserving mining of frequent closed patterns using ZDDs
Sub Title (in English)
Keyword(1) Closed pattern
Keyword(2) Distributed database
Keyword(3) Privacy-preserving data mining
Keyword(4) Zero-suppressed BDD
1st Author's Name Keisuke OOTAKI
1st Author's Affiliation Faculty of Engineering, Kyoto University()
2nd Author's Name Akihiro YAMAMOTO
2nd Author's Affiliation Graduate School of Informatics, Kyoto University
Date 2011-03-28
Paper # IBISML2010-111
Volume (vol) vol.110
Number (no) 476
Page pp.pp.-
#Pages 6
Date of Issue