Abstract
In survey statistics, simulation studies are usually performed by repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to support the theory by practical examples. However, real population data containing the information of interest are in general not available, therefore synthetic data need to be generated. Ensuring data confidentiality is thereby absolutely essential, while the simulated data should be as realistic as possible. This paper briefly outlines a recently proposed method for generating close-to-reality population data for complex (household) surveys, which is applied to generate a population for Austrian EU-SILC (European Union Statistics on Income and Living Conditions) data. Based on this synthetic population, confidentiality issues are discussed using five different worst case scenarios. In all scenarios, the intruder has the complete information on key variables from the real survey data. It is shown that even in these worst case scenarios the synthetic population data are confidential. In addition, the synthetic data are of high quality.
This work was partly funded by the European Union (represented by the European Commission) within the 7th framework programme for research (Theme 8, Socio-Economic Sciences and Humanities, Project AMELI (Advanced Methodology for European Laeken Indicators), Grant Agreement No. 217322). For more information on the project, visit http://ameli.surveystatistics.net
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Templ, M., Alfons, A. (2010). Disclosure Risk of Synthetic Population Data with Application in the Case of EU-SILC. In: Domingo-Ferrer, J., Magkos, E. (eds) Privacy in Statistical Databases. PSD 2010. Lecture Notes in Computer Science, vol 6344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15838-4_16
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