| Project/Area Number |
21K17746
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 60080:Database-related
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| Research Institution | Ochanomizu University (2023)
Tokyo Institute of Technology (2021-2022) |
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Principal Investigator |
Le Hieu Hanh お茶の水女子大学, 文理融合 AI・データサイエンスセンター, 准教授 (60813996)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | シーケンス解析 / 電子カルテ / データ保護 / data mining / privacy / medical data / differential privacy / recommendation |
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| Outline of Research at the Start |
This study aims to present a method for eliminating the need for trust in SPM while preserving privacy and providing secure, precise, and fast sequential data analysis that carefully learns the data distribution. The execution time should be reduced via parallel computation that utilizes modern hardware such as scalable multi-core CPUs. The feasibility of the proposed method will be studied using both open datasets and real medical data.
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| Outline of Final Research Achievements |
Secure sequential data analysis of sequential medical data has been extensively studied. In detail, several methods to analyze the sequence variants (SV) from more than one hospital have been designed, such as comparing SVs, identifying factors that led to branches in SVs, etc. The proposed methods were rigorously evaluated using real hospital medical data on multiple diseases.
Then, an appropriate amount of noise is added to the original frequency to ensure privacy when estimating the frequency of the sequences. Only related data is added to the analysis based on data distribution and medical meaningfulness. Finally, a secure experimental environment using the cloud in which the data access control is carefully managed has been suggested for a secure data analysis.
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| Academic Significance and Societal Importance of the Research Achievements |
この研究は、セキュアなシーケンシャルデータ解析に大きな波及効果をもたらす。これにより、医療や小売業など多くのビジネスにおいて、安全にカスタマイズ可能なツールやサービスを提供するアプリケーションの範囲が拡大できる。顧客向けのサービスや製品を安全にカスタマイズするだけでなく、産業企業内のサプライチェーン管理を効率的に最適化する可能性を見せることができる。
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