Tue, 21 Nov|
A. Kowarik , B. Meindl, J. Gussenbauer on "Statistical disclosure control and synthetic data generation using R"
In this EMOS webinar, Alexander Kowarik, Mag. Bernhard Meindl and DI Johannes Gussenbauer will present about synthetic data generation using the R software.
Time & Location
21 Nov, 16:00 GMT+1
The three presenters are working at the methods unit at Statistics Austria and are (co-)authors of the presented R software.
In this webinar, we will
- present the basic workflow in R on the application of statistical disclosure methods to tabular and micro data with sdcTable, cellKey and sdcMicro
- show the process of generating a synthetic data set from samples, census micro data and/or marginal tables with the R package simPop.
- start by discussing the importance of statistical disclosure control (SDC) in protecting the confidentiality of sensitive data.
- introduce important SDC methods and we will show how to implement them in R using the sdcTable, cellKey and sdcMicro packages.
We will then turn to the topic of synthetic data generation. Synthetic data is a type of SDC method that creates a new data set that is statistically similar to the original data but does not contain identifiable information. We will show how to generate synthetic data from samples, census micro data and/or marginal tables using the R package simPop.
This webinar is intended for researchers and practitioners who are interested in learning more about SDC and synthetic data generation in R.
It will provide researchers and practitioners with an overview of statistical disclosure control (SDC) and synthetic data generation in R.
Prerequisites and further readings:
Participants should have basic knowledge of R.
https://cran.r-project.org/web/packages/cellKey/vignettes/introduction.html https://cran.r-project.org/web/packages/sdcTable/vignettes/sdcTable.html https://cran.r-project.org/web/packages/sdcMicro/vignettes/recordSwapping.html https://cran.r-project.org/web/packages/sdcMicro/vignettes/sdcMicro.html https://www.jstatsoft.org/article/view/v079i10