  Kaplan-Meier estimators are essential tools in survival analysis, capturing
the survival behavior of a cohort. Their accuracy improves with large, diverse
datasets, encouraging data holders to collaborate for more precise estimations.
However, these datasets often contain sensitive individual information,
necessitating stringent data protection measures that preclude naive data
sharing.
  In this work, we introduce two novel differentially private methods that
offer flexibility in applying differential privacy to various functions of the
data. Additionally, we propose a synthetic dataset generation technique that
enables easy and rapid conversion between different data representations.
Utilizing these methods, we propose various paths that allow a joint estimation
of the Kaplan-Meier curves with strict privacy guarantees. Our contribution
includes a taxonomy of methods for this task and an extensive experimental
exploration and evaluation based on this structure. We demonstrate that our
approach can construct a joint, global Kaplan-Meier estimator that adheres to
strict privacy standards ($\varepsilon = 1$) while exhibiting no statistically
significant deviation from the nonprivate centralized estimator.
