Differentially Private Survival Function Estimation

TL;DR

This paper introduces the first differentially private estimator for the survival function, ensuring privacy in sensitive medical data analysis while maintaining utility, and extends it to related statistical measures with empirical validation.

Contribution

It presents a novel differentially private survival function estimator and extensions for related metrics, with empirical validation on real clinical datasets.

Findings

Provides strong privacy guarantees with good utility.

Can be extended to confidence intervals and test statistics.

Validated on eleven real-life clinical datasets.

Abstract

Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator. Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern. We propose a first differentially private estimator of the survival function and show that it can be easily extended to provide differentially private confidence intervals and test statistics without spending any extra privacy budget. We further provide extensions for differentially private estimation of the competing risk cumulative incidence function, Nelson-Aalen's estimator for the hazard function, etc. Using eleven real-life clinical datasets, we provide empirical evidence that our proposed method provides good utility while simultaneously providing strong privacy guarantees.