Presentation 2013/7/11
The Case Study of Privacy-Preserving Data Mining without Common Identification from the JPHC Study Data Set
SHIGETA IKUJI, MAKOTO KAWAMURA, TAKASHI UOZUMI, TAKAMI AZUMA, HIROAKI KIKUCHI, MANAMI INOUE,
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Abstract(in English) In the fields of medical information, an integration of the multiple data sets will lead more accu-rate and effective results in comparison to the research using one data set. Medical information integration has a risk of disclosure of confidential information and hence datasets don't have the common identification information in order to reduce possible risk factor to determine the identity of an individual. In this paper, we will examine the condition of privacy-preserving data mining for the actual cohort, studied in the National Cancer Center. We study the necessary condition for data sets without common identification information to be integrated and generating more accurate and effective results.
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Paper # Vol.2013-CSEC-62 No.57,Vol2013-SPT-6 No.57
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Committee ISEC
Conference Date 2013/7/11(1days)
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Registration To Information Security (ISEC)
Language JPN
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Title (in English) The Case Study of Privacy-Preserving Data Mining without Common Identification from the JPHC Study Data Set
Sub Title (in English)
Keyword(1)
1st Author's Name SHIGETA IKUJI
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2nd Author's Name MAKOTO KAWAMURA
2nd Author's Affiliation
3rd Author's Name TAKASHI UOZUMI
3rd Author's Affiliation
4th Author's Name TAKAMI AZUMA
4th Author's Affiliation
5th Author's Name HIROAKI KIKUCHI
5th Author's Affiliation
6th Author's Name MANAMI INOUE
6th Author's Affiliation
Date 2013/7/11
Paper # Vol.2013-CSEC-62 No.57,Vol2013-SPT-6 No.57
Volume (vol) vol.113
Number (no) 135
Page pp.pp.-
#Pages 8
Date of Issue