# Federated Survival Analysis with Node-Level Differential Privacy: Private Kaplan-Meier Curves

arXiv: https://arxiv.org/abs/2509.00615 · HTML: https://mlsanity.com/paper/2509.00615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis](https://mlsanity.com/paper/2412.05164/) (2024)
- [Differentially Private Survival Function Estimation](https://mlsanity.com/paper/1910.05108/) (2019)
- [Private and Collaborative Kaplan-Meier Estimators](https://mlsanity.com/paper/2305.15359/) (2023)
- [A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis](https://mlsanity.com/paper/2412.20495/) (2024)

## Abstract (arXiv)

We investigate how to calculate Kaplan-Meier survival curves across multiple health-care jurisdictions while protecting patient privacy with node-level differential privacy. Each site discloses its curve only once, adding Laplace noise whose scale is determined by the length of the common time grid; the server then averages the noisy curves, so the overall privacy budget remains unchanged. We benchmark four one-shot smoothing techniques: Discrete Cosine Transform, Haar Wavelet shrinkage, adaptive Total-Variation denoising, and a parametric Weibull fit on the NCCTG lung-cancer cohort under five privacy levels and three partition scenarios (uniform, moderately skewed, highly imbalanced). Total-Variation gives the best mean accuracy, whereas the frequency-domain smoothers offer stronger worst-case robustness and the Weibull model shows the most stable behaviour at the strictest privacy setting. Across all methods the released curves keep the empirical log-rank type-I error below fifteen percent for privacy budgets of 0.5 and higher, demonstrating that clinically useful survival information can be shared without iterative training or heavy cryptography.