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    This study introduces a secure Cox regression protocol for analyzing survival data across multiple providers. The new method protects sensitive information and trained models, enabling reliable multicenter medical research.

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    Area of Science:

    • Biostatistics
    • Data Privacy
    • Medical Informatics

    Background:

    • Cox proportional hazards model is crucial for survival data analysis.
    • Sharing data across multiple providers enhances Cox analysis generalizability but risks sensitive information leakage.
    • Existing privacy-preserving protocols lack sufficient security or functionality.

    Purpose of the Study:

    • To propose a novel privacy-preserving Cox regression protocol for collaborative analysis.
    • To enable secure model training on horizontally or vertically partitioned datasets.
    • To protect sensitive data and trained models in multicenter research.

    Main Methods:

    • Developed a privacy-preserving Cox regression protocol using threshold homomorphic encryption.
    • The protocol supports training on distributed datasets (horizontal and vertical partitioning).
    • Ensures privacy for both input data and the resulting statistical models.

    Main Results:

    • The proposed protocol achieves robust privacy protection for sensitive data and models.
    • Cox regression model training with 9 variables and 113,035 samples took approximately 44 minutes.
    • The trained model accuracy is comparable to non-secure Cox regression methods.

    Conclusions:

    • The developed protocol offers a secure and functional solution for privacy-preserving Cox regression.
    • It is suitable for practical applications in multicenter medical research requiring data collaboration.
    • Enables enhanced data generalizability and confidence without compromising data privacy.