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An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research.

Amanda M Y Chu1, Benson S Y Lam2, Agnes Tiwari3,4

  • 1Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong, China.

International Journal of Environmental Research and Public Health
|November 17, 2019
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Statistical disclosure control (SDC) methods like GADP and CGADP protect sensitive health data. These techniques ensure data utility for research while preventing individual re-identification, crucial for public health studies.

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data perturbationdata privacydata utilityhealth carerisk

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Area of Science:

  • Public Health
  • Data Science
  • Biostatistics

Background:

  • Patient data from health surveys holds significant research value.
  • Sensitive personal information in datasets poses privacy risks, hindering research.
  • Standard de-identification methods may not fully prevent individual re-identification.

Purpose of the Study:

  • To discuss the statistical properties of two Statistical Disclosure Control (SDC) methods.
  • To evaluate the effectiveness of General Additive Data Perturbation (GADP) and Gaussian Copula General Additive Data Perturbation (CGADP) methods.
  • To demonstrate the application of these SDC methods in public health research.

Main Methods:

  • Focus on two SDC techniques: General Additive Data Perturbation (GADP) and Gaussian Copula General Additive Data Perturbation (CGADP).
  • Analysis of statistical properties of the chosen SDC methods.
  • Empirical study to illustrate practical application in public health contexts.

Main Results:

  • The study discusses the statistical properties of GADP and CGADP methods.
  • An empirical study demonstrates the application of these methods.
  • The research highlights how SDC methods balance data utility with privacy protection.

Conclusions:

  • Statistical Disclosure Control (SDC) methods are essential for protecting sensitive health information in research datasets.
  • GADP and CGADP offer viable solutions for data privacy in public health research.
  • These methods enable the safe use of valuable health data for scientific advancement.