在保护隐私的前提下，实现跨机构生存分析的高效共享。
Federated Survival Analysis with Node-Level Differential Privacy: Private Kaplan-Meier Curves
- 各机构一次性发布带拉普拉斯噪声的生存曲线，服务器平均后保持整体隐私预算不变。
- 在隐私预算0.5以上时，所有方法的检验误差均低于15%，可满足临床需求。
- 变分平滑法精度最高，频域方法鲁棒性强，威布尔模型在严苛隐私下最稳定。
我们研究如何在保护患者隐私的前提下，跨多个医疗辖区计算Kaplan-Meier生存曲线。每个机构仅一次披露其曲线，添加拉普拉斯噪声，噪声尺度由公共时间网格长度决定；服务器对噪声曲线进行平均，确保整体隐私预算不变。我们在五种隐私级别和三种数据分布场景（均匀、中度偏斜、严重不均衡）下，基于NCCTG肺癌队列对四种一次性平滑技术进行了基准测试：离散余弦变换、哈尔小波去噪、自适应总变差去噪，以及威布尔参数拟合。总变差方法在平均精度上表现最佳，频域平滑器在最坏情况下的鲁棒性更强，而威布尔模型在最严格的隐私设置下表现出最稳定的性能。所有方法在隐私预算≥0.5时，均将经验型log-rank检验第一类错误控制在15%以下，证明了无需迭代训练或复杂加密即可安全共享具有临床价值的生存信息。
原文摘要 · Abstract (English)
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.
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