Premium
A Survey of Differentially Private Regression for Clinical and Epidemiological Research
Author(s) -
Ficek Joseph,
Wang Wei,
Chen Henian,
Dagne Getachew,
Daley Ellen
Publication year - 2021
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12391
Subject(s) - differential privacy , statistical inference , inference , data science , epidemiology , relevance (law) , regression analysis , analytics , computer science , causal inference , data mining , econometrics , medicine , statistics , mathematics , machine learning , artificial intelligence , pathology , political science , law
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.