Differentially Private Survival Function Estimation
This work addresses privacy concerns for medical analytics by enabling differentially private survival analysis, which is incremental as it extends existing methods to incorporate privacy without extra budget.
The authors tackled the problem of privacy leakage in survival function estimation, particularly for sensitive medical data, by proposing the first differentially private estimator for survival functions and related statistics, achieving good utility on eleven clinical datasets while ensuring strong privacy guarantees.
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.