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

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.

Statistical disclosure control for continuous variables using an extended skew‐t copula

“…Non‐confidential data can be age and years of work experience of customers. Another example of confidential variables is from 12 where we see the responses to five sleep‐related questions in a clinical trial regarded as confidential, and height and weight of participants in the clinical trial taken as non‐confidential.…”

“…Reference 11 developed and released an open R package synthpop to public and businesses for their easy generation of synthetic data. Apart from business applications, some examples of recent applications of SDC methods to healthcare data and research can be found in References 12,13. An interesting application of SDC methods to healthcare research can be found in Reference 14, which introduces a method to produce synthetic medical image data to allow more patient data to be used without privacy concerns.…”