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.

中文翻译：

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临床和流行病学研究的差异私密回归研究

差异隐私是用于数据分析的框架，可为数据库参与者提供严格的隐私保护。它已被越来越多地接受为分析行业隐私的黄金标准，但是在健康科学中很少有适合统计推断的技术。回归尤其如此，回归是临床和流行病学研究中使用最广泛的建模工具之一。本文概述了差异性隐私并调查了差异性私有回归的文献，重点介绍了在临床和流行病学研究中与统计推断最相关的技术。确定了研究差距和进一步调查的机会。