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

Permanent link
https://hdl.handle.net/10037/36213
DOI
https://doi.org/10.48550/arXiv.2412.20495
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Date
2024-12-29
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Veeraragavan, Narasimha Raghavan; Boudko, Svetlana; Nygård, Jan Franz
Abstract
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× 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.
Publisher
Cornell University
Citation
Veeraragavan, Boudko, Nygård. A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis. arXiv. 2024
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