# A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis

arXiv: https://arxiv.org/abs/2412.20495 · HTML: https://mlsanity.com/paper/2412.20495/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Private and Collaborative Kaplan-Meier Estimators](https://mlsanity.com/paper/2305.15359/) (2023)

## Built on by (most cited first, 2 of 2)

- [Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing](https://mlsanity.com/paper/2506.20000/) (2025)
- [Federated Survival Analysis with Node-Level Differential Privacy: Private Kaplan-Meier Curves](https://mlsanity.com/paper/2509.00615/) (2025)

## Abstract (arXiv)

The proliferation of real-world health data enables multi-institutional survival studies, yet privacy constraints preclude centralizing sensitive records. We present a privacy-preserving federated Kaplan--Meier framework based on threshold CKKS (Cheon-Kim-Kim-Song) homomorphic encryption that supports approximate floating-point computation and encrypted aggregation of per-time-point counts while exposing only public outputs. Sites compute aligned at-risk and event tallies on a shared time grid and encrypt compact vectors; a coordinator aggregates ciphertexts; and a decryptor committee produces partial shares fused per block to recover aggregated plaintexts without releasing per-time-point tables. We prove correctness, stability, and slot-optimal vector packing, and derive scaling laws showing that communication grows linearly with the number of sites and predictably with the number of time points. Empirically, using synthetic breast-cancer data (N=60,000) distributed across 500 sites, encrypted federated curves match the pooled oracle to numerical precision. In contrast, plaintext protocols permit trivial reconstruction by subtraction; our threshold-gated design precludes this attack under the stated threat model, enabling high-fidelity survival estimation with predictable overhead and substantially reduced privacy risk.