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10.1186/s13040-021-00238-x http://scihub22266oqcxt.onion/10.1186/s13040-021-00238-x 33482874!7820106!33482874     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BioData+Min 2021 ; 14 (1): 6 Nephropedia Template TP {{tp|p=33482874|t=2021. Privacy-preserving chi-squared test of independence for small samples |pdf=|usr=}}{{33482874}} gab.com Text Privacy-preserving chi-squared test of independence for small samples JO: BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=33482874 #FOAvip 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 (chi(2)) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of chi(2) tests of SNPs or personal data could lead to privacy violations. Several studies have proposed anonymization methods for chi(2) testing with epsilon-differential privacy, which is the cryptographic community's de facto privacy metric. However, existing methods can only be applied to 2x2 or 2x3 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 chi(2) 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 chi(2) values for an IxJ contingency table with a small number of samples. The experimental results show that RandChiDist outperforms existing methods for small numbers of samples. Twit Text FOAVip Privacy-preserving chi-squared test of independence for small samples ????BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=33482874 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33482874 #FOAvip English Wikipedia <ref>{{Cite pmid|33482874}}</ref> Privacy-preserving chi-squared test of independence for small samples #MMPMID33482874 Sei Y; Ohsuga A BioData Min 2021[Jan]; 14 (1): 6 PMID33482874show ga 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 (chi(2)) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of chi(2) tests of SNPs or personal data could lead to privacy violations. Several studies have proposed anonymization methods for chi(2) testing with epsilon-differential privacy, which is the cryptographic community's de facto privacy metric. However, existing methods can only be applied to 2x2 or 2x3 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 chi(2) 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 chi(2) values for an IxJ contingency table with a small number of samples. The experimental results show that RandChiDist outperforms existing methods for small numbers of samples. äPrivacy-preserving chi-squared test of independence for small samples (epmc).pdf DeepDyve Pubget Overpricing