Item type |
SIG Technical Reports(1) |
公開日 |
2021-09-23 |
タイトル |
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タイトル |
Efficient Differentially Private Methods for a Transmission Disequilibrium Test |
タイトル |
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言語 |
en |
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タイトル |
Efficient Differentially Private Methods for a Transmission Disequilibrium Test |
言語 |
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言語 |
eng |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
著者所属 |
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The University of Tokyo |
著者所属 |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者所属(英) |
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en |
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The University of Tokyo |
著者名 |
Akito, Yamamoto
Tetsuo, Shibuya
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著者名(英) |
Akito, Yamamoto
Tetsuo, Shibuya
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論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
To achieve the personalized medicine, it is important to examine the links between diseases and genomes. For this purpose, large-scale genetic studies are often conducted, but there is a risk of identifying individuals. In this study, we propose new efficient differentially private methods for a transmission disequilibrium test. Existing methods are computationally intensive and take a long time even for a small cohort. Moreover, for approximation methods, sensitivity of the obtained values is not guaranteed. We first present an exact algorithm with a low time complexity, and also propose an approximation algorithm that is faster than the exact one and prove that the obtained scores' sensitivity is 1. The experimental results show that our exact algorithm is 10, 000 times faster than existing methods for a small cohort. The results also indicate that the proposed method can be applied to a sufficiently large cohort. In addition, we discuss a suitable dataset to apply our algorithms. |
論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
To achieve the personalized medicine, it is important to examine the links between diseases and genomes. For this purpose, large-scale genetic studies are often conducted, but there is a risk of identifying individuals. In this study, we propose new efficient differentially private methods for a transmission disequilibrium test. Existing methods are computationally intensive and take a long time even for a small cohort. Moreover, for approximation methods, sensitivity of the obtained values is not guaranteed. We first present an exact algorithm with a low time complexity, and also propose an approximation algorithm that is faster than the exact one and prove that the obtained scores' sensitivity is 1. The experimental results show that our exact algorithm is 10, 000 times faster than existing methods for a small cohort. The results also indicate that the proposed method can be applied to a sufficiently large cohort. In addition, we discuss a suitable dataset to apply our algorithms. |
書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12055912 |
書誌情報 |
研究報告バイオ情報学(BIO)
巻 2021-BIO-67,
号 3,
p. 1-6,
発行日 2021-09-23
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ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8590 |
Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |