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

Author

Listed:
  • Amanda M. Y. Chu

    (Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong, China)

  • Benson S. Y. Lam

    (Department of Mathematics and Statistics, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China)

  • Agnes Tiwari

    (School of Nursing, The University of Hong Kong, Pokfulam Road, Hong Kong, China
    School of Nursing, Hong Kong Sanatorium & Hospital, Hong Kong, China)

  • Mike K. P. So

    (Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

Abstract

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.

Suggested Citation

  • Amanda M. Y. Chu & Benson S. Y. Lam & Agnes Tiwari & Mike K. P. So, 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research," IJERPH, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:22:p:4519-:d:287403
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    References listed on IDEAS

    as
    1. Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, December.
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    Cited by:

    1. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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