2001 Fiscal Year Final Research Report Summary
Local disclosure control techniques of statistical microdata sets
| Project/Area Number |
11558026
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
TAKEMURA Akimichi Graduate School of Information Science and Technology, The University of Tokyo, Professor, 大学院・情報理工学系研究科, 教授 (10171670)
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| Co-Investigator(Kenkyū-buntansha) |
FUKUSHIGE Mototsugu Kobe University, Faculty of Economics, Associate Professor, 経済学部, 助教授 (10208936)
OOMORI Yasuhiro Tokyo Metropolitan University, Faculty of Economies, Associate Professor, 経済学部, 助教授 (60251188)
UCHINAMI Seiichi Kyushu Institute of Technology, Faculty of Computer Science and Systems, Professor, 情報工学部, 教授 (00029240)
HOSHINO Nobuaki Kanazawa University, Faculty of Economics, Lecturer, 経済学部, 講師 (00313627)
TAKI Atsuhiro Hiroshima University, Faculty of Economics, Associate Professor, 経済学部, 助教授 (40216809)
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| Project Period (FY) |
1999 – 2001
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| Keywords | Superpopulation model / Population unique / Multinomial distribution / Dirichlet distribution |
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| Research Abstract |
We obtained several important results on the disclosure control techniques of statistical microdata sets in this year.
Takemura gave an invited talk on the paper "Evaluation of per-record identification risk by additive modeling of interaction for contingency table cell probabilities" at the meeting of International Statistical Institute. This paper will be included in a separate proceedings published by International Association of Survey Statisticians. A paper "Minimum unsafe and maximum safe sets of variables for disclosure risk assessment of individual records in a microdata set" will be published in Journal of Japan Statistical Society The paper "On application of Pitman's sampling formula to microdata disclosure risk assessment" by Nobuaki Hoshino was published in Journal of Official Statistics. As a new research results, two discussion papers "On the random clustering with the conditional inverse Gaussian-Poisson distribution" and "On limiting random partition structure derived from the conditional inverse Gaussian-Poisson distribution" are published as discussion paper in Carnegie Mellon university.
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