In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example.

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Summary. Data holders can produce synthetic versions of data sets when concerns about potential disclosure restrict the availability of the original records. The paper is concerned with methods to judge whether such synthetic data have a distribution that is comparable with that of the original data: what we term general utility. We consider how general utility compares with specific utility: the similarity of results of analyses from the synthetic data and the original data. We adapt a previous general measure of data utility, the propensity score mean-squared error pMSE, to the specific case of synthetic data and derive its distribution for the case when the correct synthesis model is used to create the synthetic data. Our asymptotic results are confirmed by a simulation study. We also consider two specific utility measures, confidence interval overlap and standardized difference in summary statistics, which we compare with the general utility results. We present two contrasting examples of data syntheses: one illustrating synthetic data that is evaluated as being useful by both general and specific measures and the second where neither is the case. For the second case we show how the general utility measures can identify the deficiencies of the synthetic data and suggest how this can inform possible improvements to the synthesis method.

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 findings. An example of synthesising data from the Scottish Longitudinal Study is included to illustrate our results.

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