Next Article in Journal
Intersectional Discrimination Is Associated with Housing Instability among Trans Women Living in the San Francisco Bay Area
Next Article in Special Issue
The Impacts of Green Innovation Input and Channel Service in a Dual-Channel Value Chain
Previous Article in Journal
In-Season Weightlifting Training Exercise in Healthy Male Handball Players: Effects on Body Composition, Muscle Volume, Maximal Strength, and Ball-Throwing Velocity
Previous Article in Special Issue
Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality
Open AccessArticle

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

1
Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong, China
2
Department of Mathematics and Statistics, The Hang Seng University of Hong Kong, Shatin, Hong Kong, China
3
School of Nursing, The University of Hong Kong, Pokfulam Road, Hong Kong, China
4
School of Nursing, Hong Kong Sanatorium & Hospital, Hong Kong, China
5
Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(22), 4519; https://doi.org/10.3390/ijerph16224519
Received: 14 October 2019 / Revised: 3 November 2019 / Accepted: 4 November 2019 / Published: 15 November 2019
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. View Full-Text
Keywords: data perturbation; data privacy; data utility; health care; risk data perturbation; data privacy; data utility; health care; risk
Show Figures

Figure 1

MDPI and ACS Style

Chu, A.M.Y.; Lam, B.S.Y.; Tiwari, A.; So, M.K.P. An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research. Int. J. Environ. Res. Public Health 2019, 16, 4519. https://doi.org/10.3390/ijerph16224519

AMA Style

Chu AMY, Lam BSY, Tiwari A, So MKP. An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research. International Journal of Environmental Research and Public Health. 2019; 16(22):4519. https://doi.org/10.3390/ijerph16224519

Chicago/Turabian Style

Chu, Amanda M.Y.; Lam, Benson S.Y.; Tiwari, Agnes; So, Mike K.P. 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research" Int. J. Environ. Res. Public Health 16, no. 22: 4519. https://doi.org/10.3390/ijerph16224519

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop