{"ID":2876556,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00615","arxiv_id":"2509.00615","title":"Federated Survival Analysis with Node-Level Differential Privacy: Private Kaplan-Meier Curves","abstract":"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.","short_abstract":"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 ave...","url_abs":"https://arxiv.org/abs/2509.00615","url_pdf":"https://arxiv.org/pdf/2509.00615v1","authors":"[\"Narasimha Raghavan Veeraragavan\",\"Jan Franz Nygård\"]","published":"2025-08-30T21:47:56Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.DC\",\"cs.LG\"]","methods":"[]","has_code":false}