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  11. Preserving Information While Respecting Privacy Through An Information Theoretic Framework For Synthetic Health Data Generation

Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation

Nadir Sella1, Florent Guinot2, Nikita Lagrange3

  • 1Institut Roche, Boulogne-Billancourt, France. nadirsella@gmail.com.

NPJ Digital Medicine
|January 22, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces MIIC-SDG, a novel algorithm for generating high-quality, privacy-preserving synthetic medical data using a multivariate information framework. It also presents a new metric to evaluate the trade-off between data quality and privacy in synthetic data generation.

Area of Science:

  • Medical Informatics
  • Data Science
  • Biostatistics

Background:

  • Generating synthetic medical data is challenging due to privacy concerns.
  • Existing methods often fail to jointly evaluate data quality and privacy.
  • Assessing multivariate associations is crucial for accurate synthetic data.

Purpose of the Study:

  • Introduce a novel algorithm (MIIC-SDG) for synthetic data generation from electronic health records.
  • Propose a new metric (QPS) to quantify the quality-privacy trade-off.
  • Demonstrate the performance of MIIC-SDG against state-of-the-art methods.

Main Methods:

  • Developed MIIC-SDG based on multivariate information theory and Bayesian networks.
  • Proposed a Quality and Privacy Score (QPS) metric for evaluation.

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  • Validated MIIC-SDG on diverse clinical datasets.

    • Main Results:

    • MIIC-SDG effectively generates synthetic data preserving multivariate associations.
    • The QPS metric provides a quantitative measure of the quality-privacy balance.
    • MIIC-SDG demonstrates superior performance compared to existing methods.

    Conclusions:

    • MIIC-SDG offers a robust approach for generating high-fidelity, privacy-preserving synthetic medical data.
    • The QPS metric facilitates objective comparison of synthetic data generation techniques.
    • This work advances the field of secure and reliable health data synthesis.