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Population Data Centre Profiles: Centre for Data Linkage.

J H Boyd1,2, S M Randall1, A P Brown1

  • 1Centre for Data Linkage, School of Public Health, Curtin University.

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|September 16, 2020
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Summary
This summary is machine-generated.

The Centre for Data Linkage developed privacy-preserving record linkage methods and software for complex data. Distributed models offer a solution for a national linkage system in Australia.

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Area of Science:

  • Health Informatics
  • Data Science
  • Biostatistics

Background:

  • Established at Curtin University, the Centre for Data Linkage (CDL) aimed to build infrastructure for cross-jurisdictional record linkage in Australia.
  • The CDL operates using the 'separation principle,' where data custodians provide content data directly to researchers.

Purpose of the Study:

  • To develop infrastructure for enabling cross-jurisdictional record linkage across Australia.
  • To advance record linkage methodology and develop modern software capable of handling large, complex datasets.
  • To pioneer practical methods for privacy-preserving record linkage.

Main Methods:

  • Utilized the 'separation principle' for data handling.
  • Developed and implemented modern record linkage software.
  • Invested in a research program for record linkage methodology.
  • Applied privacy-preserving techniques in real-world linkage scenarios.

Main Results:

  • Successfully developed practical methods for privacy-preserving record linkage.
  • Created modern record linkage software designed for large-scale data.
  • Facilitated real-world data linkages using developed methodologies.
  • Demonstrated the utility of distributed models as a potential solution for national linkage.

Conclusions:

  • The Centre for Data Linkage has made significant contributions to privacy-preserving record linkage and software development.
  • Distributed models present a viable approach to achieving a national linkage system in Australia.
  • Continued investment in methodology and software is crucial for addressing complex data linkage challenges.