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

[2412.20495] A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2412.20495 (cs) [Submitted on 29 Dec 2024 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Authors: Narasimha Raghavan Veeraragavan , Svetlana Boudko , Jan Franz Nygård View a PDF of the paper titled A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis, by Narasimha Raghavan Veeraragavan and 2 other authors View PDF HTML (experimental) Abstract: 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. Comments: 58 pages Subjects: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2412.20495 [cs.CR] (or arXiv:2412.20495v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2412.20495 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Narasimha Raghavan Veeraragavan [ view email ] [v1] Sun, 29 Dec 2024 15:17:42 UTC (3,116 KB) [v2] Sat, 11 Apr 2026 23:14:16 UTC (4,453 KB) Full-text links: Access Paper: View a PDF of the paper titled A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis, by Narasimha Raghavan Veeraragavan and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2024-12 Change to browse by: cs cs.AI cs.LG stat stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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