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A Survey of Differentially Private Regression for Clinical and Epidemiological Research

Author

Listed:
  • Joseph Ficek
  • Wei Wang
  • Henian Chen
  • Getachew Dagne
  • Ellen Daley

Abstract

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.

Suggested Citation

  • Joseph Ficek & Wei Wang & Henian Chen & Getachew Dagne & Ellen Daley, 2021. "A Survey of Differentially Private Regression for Clinical and Epidemiological Research," International Statistical Review, International Statistical Institute, vol. 89(1), pages 132-147, April.
    • Handle: RePEc:bla:istatr:v:89:y:2021:i:1:p:132-147
    DOI: 10.1111/insr.12391
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    References listed on IDEAS

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    1. Darren B Taichman & Peush Sahni & Anja Pinborg & Larry Peiperl & Christine Laine & Astrid James & Sung-Tae Hong & Abraham Haileamlak & Laragh Gollogly & Fiona Godlee & Frank A Frizelle & Fernando Flor, 2017. "Data Sharing Statements for Clinical Trials: A Requirement of the International Committee of Medical Journal Editors," PLOS Medicine, Public Library of Science, vol. 14(6), pages 1-3, June.
    2. repec:mpr:mprres:6177 is not listed on IDEAS
    3. Chris Skinner, 2012. "Statistical Disclosure Risk: Separating Potential and Harm," International Statistical Review, International Statistical Institute, vol. 80(3), pages 349-368, December.
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