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.
journal_name
Int J Environ Res Public Healthauthors
Chu AMY,Lam BSY,Tiwari A,So MKPdoi
10.3390/ijerph16224519subject
Has Abstractpub_date
2019-11-15 00:00:00issue
22eissn
1661-7827issn
1660-4601pii
ijerph16224519journal_volume
16pub_type
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pub_type： 杂志文章,meta分析
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pub_type： 杂志文章,随机对照试验
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更新日期：2019-12-22 00:00:00
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更新日期：2020-06-24 00:00:00
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更新日期：2020-05-04 00:00:00
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