A Survey of Differentially Private Regression for Clinical and Epidemiological Research. (27th July 2020)

• Record Type: Journal Article
• Authors: Ficek, Joseph; Wang, Wei; Chen, Henian; Dagne, Getachew; Daley, Ellen
• Is Part Of: International statistical review. Volume 89:Number 1(2021)
• Page Start: 132
• Page End: 147
• Publication Date: 2020-07-27
• Subjects: confidence intervals -- data confidentiality -- data privacy -- differential privacy -- hypothesis testing -- regression -- statistical disclosure limitation; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12391
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: Differential privacy is a framework for data analysis that provides rigorous privacy protections for database participants. It has increasingly been accepted as the gold standard for privacy in the analytics industry, yet there are few techniques suitable for statistical inference in the health sciences. This is notably the case for regression, one of the most widely used modelling tools in clinical and epidemiological studies. This paper provides an overview of differential privacy and surveys the literature on differentially private regression, highlighting the techniques that hold the most relevance for statistical inference as practiced in clinical and epidemiological research. Research gaps and opportunities for further inquiry are identified.