The proliferation of healthcare data has expanded opportunities for collaborative research, yet stringent privacy regulations hinder pooling sensitive patient records. We propose a \emph{multiparty homomorphic encryption-based} framework for \emph{privacy-preserving federated Kaplan--Meier survival analysis}, offering native floating-point support, a theoretical model, and explicit reconstruction-attack mitigation. Compared to prior work, our framework ensures encrypted federated survival estimates closely match centralized outcomes, supported by formal utility-loss bounds that demonstrate convergence as aggregation and decryption noise diminish. Extensive experiments on the NCCTG Lung Cancer and synthetic Breast Cancer datasets confirm low \emph{mean absolute error (MAE)} and \emph{root mean squared error (RMSE)}, indicating negligible deviations between encrypted and non-encrypted survival curves. Log-rank and numerical accuracy tests reveal \emph{no significant difference} between federated encrypted and non-encrypted analyses, preserving statistical validity. A reconstruction-attack evaluation shows smaller federations (2--3 providers) with overlapping data between the institutions are vulnerable, a challenge mitigated by multiparty encryption. Larger federations (5--50 sites) degrade reconstruction accuracy further, with encryption improving confidentiality. Despite an 8--19$\times$ computational overhead, threshold-based homomorphic encryption is \emph{feasible for moderate-scale deployments}, balancing security and runtime. By providing robust privacy guarantees alongside high-fidelity survival estimates, our framework advances the state-of-the art in secure multi-institutional survival analysis.

本研究解决了在严格隐私法规下共享敏感医疗数据的挑战，提出了一种基于多方同态加密的隐私保护联邦Kaplan-Meier生存分析框架。新方法确保加密的联邦生存估计与集中结果高度一致，并通过实验验证了其在多个数据集上表现出色，具有较小的均绝对误差和均方根误差，展现出在保护数据隐私和实现高保真生存估计方面的显著潜力。

多方同态加密方法在保密的联邦Kaplan-Meier生存分析中的应用