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A Secure Multi-Party Computation Protocol for Time-To-Event Analyses.

Lennart Vogelsang1,2, Moritz Lehne3, Phillipp Schoppmann1,2

  • 1Department of Computer Science, Humboldt-Universität zu Berlin, Germany.

Studies in Health Technology and Informatics
|June 24, 2020
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Summary
This summary is machine-generated.

Secure Multi-Party Computation (SMPC) enables secure health data analysis without sharing private information. A novel, optimized SMPC protocol for time-to-event analysis demonstrates practical and scalable performance, facilitating collaborative research.

Keywords:
healthcare dataoperational confidentiality of informationpatient privacysecure multi-party computation

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Area of Science:

  • Health Informatics
  • Cryptography
  • Biostatistics

Background:

  • Secure Multi-Party Computation (SMPC) allows distributed data analysis without data disclosure.
  • Developing efficient, analysis-specific SMPC protocols is crucial for practical application.
  • Existing SMPC methods require optimization for acceptable execution times in health data science.

Purpose of the Study:

  • To present a novel, efficient protocol for time-to-event analysis using SMPC.
  • To optimize the protocol for reduced execution times.
  • To evaluate the practicality and scalability of the SMPC approach for health data analysis.

Main Methods:

  • Developed a novel cryptographic protocol for time-to-event analysis using garbled circuits.
  • Implemented the protocol within a widespread SMPC programming framework.
  • Introduced specific optimizations to enhance protocol efficiency and reduce execution times.

Main Results:

  • Experimental evaluation computed Kaplan-Meier estimators over a vertically distributed dataset.
  • Performance was measured and compared against conventional analysis on pooled data.
  • The SMPC approach demonstrated practical feasibility and scalability for health data analysis.

Conclusions:

  • The developed SMPC protocol offers an efficient method for time-to-event analysis on distributed health data.
  • Optimizations significantly reduce execution times, making the approach viable for real-world applications.
  • This work represents a foundational step towards a library of efficient SMPC methods for health data science.