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Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC.

Marius de Arruda Botelho1,2,3, Cem Ata Baykara2, Ali Burak Ünal2

  • 1Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.

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Summary
This summary is machine-generated.

We developed two privacy-preserving methods for calculating the Area Under the Curve (AUC) in distributed machine learning. These solutions enable secure AUC computation across institutions without sharing sensitive data, enhancing privacy in healthcare analytics.

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Area of Science:

  • Distributed machine learning
  • Computational privacy
  • Healthcare informatics

Background:

  • Computing Area Under the Curve (AUC) in distributed machine learning is challenging due to data privacy concerns.
  • Existing cryptographic methods for AUC computation may sacrifice scalability or accuracy.
  • Sensitive test data pooling is often restricted in multi-institutional settings.

Purpose of the Study:

  • To present novel privacy-preserving solutions for secure AUC computation in distributed environments.
  • To enable accurate AUC calculation across multiple institutions without compromising data confidentiality.
  • To integrate and demonstrate these methods within a real-world healthcare platform.

Main Methods:

  • Developed an exact global AUC method with linear scalability and tie handling.
  • Introduced an approximation method for reduced runtime and maintained accuracy.
  • Utilized homomorphic encryption (modified Paillier), symmetric/asymmetric cryptography, and randomized encoding.
  • Integrated protocols into the Personal Health Train (PHT)-meDIC platform.

Main Results:

  • The exact method computes true AUC without revealing private inputs, handling ties effectively.
  • The approximation method offers a balance between computational efficiency and precision.
  • Demonstrated correctness and feasibility on real-world and synthetic datasets.
  • Publicly released code and data for broader adoption.

Conclusions:

  • The proposed methods successfully enable privacy-preserving AUC computation in distributed machine learning.
  • These solutions are crucial for secure analytics in sensitive domains like healthcare.
  • The integration with PHT-meDIC showcases practical applicability and facilitates further research.