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Privacy-preserving chi-squared test of independence for small samples
https://uec.repo.nii.ac.jp/records/10071
https://uec.repo.nii.ac.jp/records/100716105c49d-f21d-4c01-893d-15ada8b6c2a5
名前 / ファイル | ライセンス | アクション |
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6.pdf (2.6 MB)
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CC BY 4.0
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-10-18 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Privacy-preserving chi-squared test of independence for small samples | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Differentical privacy | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Chi-squared testing | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Privacy-preserving data mining | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Sei, Yuichi
× Sei, Yuichi× Ohsuga, Akihiko |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Background: The importance of privacy protection in analyses of personal data, such as genome-wide association studies (GWAS), has grown in recent years. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and the chi-squared (χ2) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of χ2 tests of SNPs or personal data could lead to privacy violations. Several studies have proposed anonymization methods for χ2 testing with ε-differential privacy, which is the cryptographic community’s de facto privacy metric. However, existing methods can only be applied to 2×2 or 2×3 contingency tables, otherwise their accuracy is low for small numbers of samples. It is difficult to collect numerous high-sensitive samples in many cases such as COVID-19 analysis in its early propagation stage. Results: We propose a novel anonymization method (RandChiDist), which anonymizes χ2 testing for small samples. We prove that RandChiDist satisfies differential privacy. We also experimentally evaluate its analysis using synthetic datasets and real two genomic datasets. RandChiDist achieved the least number of Type II errors among existing and baseline methods that can control the ratio of Type I errors. Conclusions: We propose a new differentially private method, named RandChiDist, for anonymizing χ2 values for an I×J contingency table with a small number of samples. The experimental results show that RandChiDist outperforms existing methods for small numbers of samples. |
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書誌情報 |
en : BioData Mining 巻 14, 号 6, p. 1-25, 発行日 2021-01-22 |
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出版者 | ||||||
出版者 | BioMed Central (BMC) | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 17560381 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1186/s13040-021-00238-x | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1186/s13040-021-00238-x | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |