An empirical study of applying statistical disclosure control methods to public health research

Man Ying Amanda CHU, Benson S. Y. LAM, Agnes TIWARI, Mike K. P. SO

Research output: Contribution to journalArticle


Patient data or information collected from public health and health care surveys are of great research value. Usually, the data contain sensitive personal information. Doctors, nurses, or researchers in the public health and health care sector do not analyze the available datasets or survey data on their own, and may outsource the tasks to third parties. Even though all identifiers such as names and ID card numbers are removed, there may still be some occasions in which an individual can be re-identified via the demographic or particular information provided in the datasets. Such data privacy issues can become an obstacle in health-related research. Statistical disclosure control (SDC) is a useful technique used to resolve this problem by masking and designing released data based on the original data. Whilst ensuring the released data can satisfy the needs of researchers for data analysis, there is high protection of the original data from disclosure. In this research, we discuss the statistical properties of two SDC methods: the General Additive Data Perturbation (GADP) method and the Gaussian Copula General Additive Data Perturbation (CGADP) method. An empirical study is provided to demonstrate how we can apply these two SDC methods in public health research. Copyright © 2019 by the authors.
Original languageEnglish
Article number4519
JournalInternational Journal of Environmental Research and Public Health
Issue number22
Publication statusPublished - Nov 2019


Public Health
Research Personnel
Health Care Surveys
Computer Security
Health Care Sector


Chu, A. M. Y., Lam, B. S. Y., Tiwari, A., & So, M. K. P. (2019). An empirical study of applying statistical disclosure control methods to public health research. International Journal of Environmental Research and Public Health, 16(22). Retrieved from


  • Data perturbation
  • Data privacy
  • Data utility
  • Health care
  • Risk