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

Amanda M. Y. Chu, Benson S. Y. Lam, Agnes Tiwari and Mike K. P. So
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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
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

IJERPH, 2019, vol. 16, issue 22, 1-17

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.

Keywords: data perturbation; data privacy; data utility; health care; risk (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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