Supporting Regularized Logistic Regression Privately and Efficiently

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 regulatio...

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Bibliographic Details
Published in:	PloS one Vol. 11; no. 6; p. e0156479
Main Authors:	Li, Wenfa, Liu, Hongzhe, Yang, Peng, Xie, Wei
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 06.06.2016 Public Library of Science (PLoS)
Subjects:	Analysis Biology and Life Sciences Collaboration Computer and Information Sciences Computer Communication Networks - organization & administration Computer Communication Networks - standards Computer Communication Networks - statistics & numerical data Computer engineering Computer Security Confidentiality Consortia Cooperative Behavior Cryptography Cybersecurity Data collection Data processing Data retrieval Distributed processing Empirical analysis Engineering and Technology Epidemiology Genetic Information Nondiscrimination Act 2008-US Genetics Genomes Health informatics Humans Information Dissemination - methods Information management Information services Information sharing Information technology Laboratories Learning algorithms Linear algebra Logistic Models Machine learning Machine Learning - standards Management Mathematical models Medicine and Health Sciences Methods Models, Statistical Network analysis Parkinson's disease Physical Sciences Privacy Quantitative genetics Regression analysis Regression models Regularization Research and Analysis Methods Security Smart grid Social Sciences Statistical analysis Statistical models United States Beijing China China
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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