用加密联邦方法实现多方生存分析，保护隐私且结果精确。
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis
- 基于阈值CKKS加密，支持加密状态下计算和聚合生存数据
- 500个机构共享数据，结果与集中式分析完全一致
- 防止数据泄露攻击，适合医疗多中心研究
真实世界健康数据推动了多机构生存研究，但隐私限制使得敏感记录无法集中。我们提出一种基于阈值CKKS同态加密的隐私保护联邦Kaplan-Meier框架，支持近似浮点计算和加密的各时间点计数聚合，仅暴露公开输出。各机构在共享时间网格上计算风险人数和事件数并加密为紧凑向量；协调者聚合密文；解密委员会按块融合部分份额以恢复明文聚合结果，不泄露各时间点明细表。我们证明了正确性、稳定性及槽位最优打包，并推导出缩放定律：通信量随机构数线性增长，随时间点数可预测增加。实验使用合成乳腺癌数据（N=60,000）分布于500个站点，加密联邦曲线与集中式基准精度一致。相比之下，明文协议可通过减法轻易重构数据；我们的门限设计在给定威胁模型下可防止此类攻击，实现高保真生存估计，具有可预测开销和显著降低的隐私风险。
原文摘要 · Abstract (English)
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.
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