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Synthetic Data

In: Statistical Disclosure Control for Microdata

Author

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  • Matthias Templ

    (Zurich University of Applied Sciences (ZHAW), Institute of Data Analysis and Process Design (IDP), School of Engineering (SoE)
    data-analysis OG)

Abstract

The generation of synthetic data sets serves as a statistical disclosure control solution to generate public use files out of confidential/protected data. In addition, it is also a tool to create “augmented data sets” which serve as input for micro-simulation models or as data sets for remote execution. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. In this chapter, the most promising and important method—model-based simulation—is described in detail. It is also the reason why whole populations are simulated rather than only surveys. For other approaches, we refer to Drechsler (2011) (Drechsler, Synthetic data sets for statistical disclosure control. Springer, New York, 2011) and other references below.

Suggested Citation

  • Matthias Templ, 2017. "Synthetic Data," Springer Books, in: Statistical Disclosure Control for Microdata, chapter 0, pages 157-179, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-50272-4_6
    DOI: 10.1007/978-3-319-50272-4_6
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