Presentation | 2011-03-28 Privacy-preserving mining of frequent closed patterns using ZDDs Keisuke OOTAKI, Akihiro YAMAMOTO, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Closed pattern / Distributed database / Privacy-preserving data mining / Zero-suppressed BDD |
Paper # | IBISML2010-111 |
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Committee | IBISML |
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Conference Date | 2011/3/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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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 |
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