| Co-Investigator(Kenkyū-buntansha) |
間野 修平 統計数理研究所, 数理・推論研究系, 教授 (20372948)
入江 薫 東京大学, 大学院経済学研究科(経済学部), 准教授 (20789169)
佐井 至道 岡山商科大学, 経済学部, 教授 (30186910)
丸山 祐造 神戸大学, 経営学研究科, 教授 (30304728)
|
|---|
| Budget Amount *help |
¥16,900,000 (Direct Cost: ¥13,000,000、Indirect Cost: ¥3,900,000)
Fiscal Year 2022: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2021: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2020: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2019: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2018: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
|
|---|
| Outline of Final Research Achievements |
The existing definition of privacy or confidentiality of microdata is technically ambiguous. Hence we have proposed a new definition of those, which is precise and easy to interpret. The breach of privacy or confidentiality is nothing but a status of a population, which is estimated based on published data. If such data are random, then the accuracy of the estimation of a population can be stochastically evaluated and summarized as a measure of, say, anonymity. We have proved that this accuracy is bounded by the parameter (budget) of privacy that is adopted in informatics. From this point of view, we have proposed a parametric methodology to randomly publish data by a new family of distributions, with clarification of high dimensional and/or large sample properties.
|
