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10.1371/journal.pone.0156479 http://scihub22266oqcxt.onion/10.1371/journal.pone.0156479 C4894560!4894560 !27271738     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27271738 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2016 ; 11 (6 ): e0156479 Nephropedia Template TP {{tp|p=27271738 |t=2016. Supporting Regularized Logistic Regression Privately and Efficiently |pdf=|usr=}}{{27271738 }} gab.com Text Supporting Regularized Logistic Regression Privately and Efficiently JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27271738 #FOAvip As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. Twit Text FOAVip Supporting Regularized Logistic Regression Privately and Efficiently ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27271738 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27271738 #FOAvip English Wikipedia <ref>{{Cite pmid|27271738 }}</ref> Supporting Regularized Logistic Regression Privately and Efficiently #MMPMID27271738 Li W ; Liu H ; Yang P ; Xie W PLoS One 2016[]; 11 (6 ): e0156479 PMID27271738 show ga As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. |*Computer Security [MESH] |*Logistic Models [MESH] |*Machine Learning/standards [MESH] |*Privacy [MESH] |Computer Communication Networks/organization & administration/standards/statistics & numerical data [MESH] |Confidentiality [MESH] |Cooperative Behavior [MESH] |Humans [MESH] |Information Dissemination/*methods [MESH]Supporting Regularized Logistic Regression Privately and Efficiently (epmc).pdf DeepDyve Pubget Overpricing