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Deep survival models can leak patient data. This study shows differential privacy protects sensitive information in shared deep learning models for survival analysis, with minimal impact on performance.

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Computational Biology

Background:

  • Deep neural networks are vital for healthcare predictions.
  • Sharing trained models aids research but risks data privacy.
  • Membership inference attacks can reveal individual data in training sets.

Purpose of the Study:

  • To investigate membership leakage in deep survival models.
  • To evaluate differential privacy for defending against inference attacks.
  • To assess differential privacy's impact on deep survival analysis performance.

Main Methods:

  • Assessed membership leakage in deep survival models.
  • Developed differentially private training procedures.
  • Quantified privacy risks and performance trade-offs.

Main Results:

  • Deep survival models were found to leak membership information.
  • Differential privacy significantly reduced membership inference risks.
  • Differential privacy introduced limited performance loss and potentially enhanced robustness.

Conclusions:

  • Deep survival models pose privacy risks.
  • Differentially private training offers effective protection for shared models.
  • Privacy-preserving methods are crucial for secure collaborative healthcare AI.