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Practical Data Synthesis for Large Samples

Gillian M Raab, Beata Nowok, Chris Dibben

The Journal of Privacy and Confidentiality · 2018

doi:10.29012/jpc.v7i3.407 (opens in a new tab)

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Abstract

We describe results on the creation and use of synthetic data that were derived in the context of a project to make synthetic extracts available for users of the UK Longitudinal Studies. A critical review of existing methods of inference from large synthetic data sets is presented. We introduce new variance estimates for use with large samples of completely synthesised data that do not require them to be generated from the posterior predictive distribution derived from the observed data and can be used with a single synthetic data set. We make recommendations on how to synthesise data based on these results. The practical consequences of these results are illustrated with an example from the Scottish Longitudinal Study.

Abstract licence: CC BY-NC-ND

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