| Project/Area Number |
15K00194
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Information security
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| Research Institution | Meiji University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
康永 秀生 東京大学, 大学院医学系研究科(医学部), 教授 (90361485)
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| Co-Investigator(Renkei-kenkyūsha) |
Hashimoto Hideki 東京大学, 医学系研究科, 教授 (50317682)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 医療情報 / ビッグデータ / プライバシー保護 / プライバシー / データマイニング / 垂直分割 / ロジスティック回帰 / 秘匿計算 / ロジステック回帰分析 / 疫学 |
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| Outline of Final Research Achievements |
In this study, we propose a new secure protocols for privacy-preserving logistic regression of two vertically partitioned datasets. Our protocol is efficient in the sense that coefficients of logistic model are converged in few iterations by using the Iteratively Re-weighted Least Squares (IRLS). In the comparison to one of the existing work using the stochastic gradient descent (SGD), our protocol improved the performance of estimate from 30,000 to 7 iterations. We study the feasibility of the proposed protocol over the the Diagnosis Procedure Combination (DPC) database, a large-scale claim-based database of Japanese hospitals that contains confidential status of patients. Our scheme allows to estimate the probability of death with some patient information without revealing confidential data to the other party. Using the toy dataset and the trial implementation of the proposed scheme, we examine the accuracy of the proposed scheme and study the feasibility.
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