To limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed; but some collected values, for example, sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple draws from statistical models. Because the original records are on the file, there remain risks of identifications. In this paper, we describe how to evaluate identification disclosure risks in partially synthetic data, accounting for released information from the multiple datasets, the model used to generate synthetic values, and the approach used to select values to synthesize. We illustrate the computations using the Survey of Youths in Custody.
ObjectivesGeneration of synthetic data could improve the efficiency of administrative data analysis. We describe barriers and facilitators to synthetic administrative data in the UK based on our experience of generating, assessing, and evaluating the performance of different approaches. We aim to provide guidance on the appropriate uses of synthetic administrative data.
ApproachWe generated synthetic versions of one large-population survey (Natsal-3) and two administrative datasets (Hospital Episode Statistics [HES] and National Pupil Database [NPD]). A range of methods were used based on the statistical techniques of sampling and prediction. We implemented non-parametric (e.g., Classification and Regression Tree) and parametric (e.g., generalised linear models) methods, and multiple imputation and Bayesian networks in R software. We attempted to generate low- and high-fidelity datasets and assessed utility by visualising marginal distributions of key variables, estimating the standardised propensity mean square error, and deriving standardised coefficient differences of model estimates and overlap of confidence intervals.
ResultsResults from our analysis highlighted some facilitators related to low-fidelity synthetic data that are quicker to generate, can retain the data types, format, and privacy and could be used to support training and code development. Conversely, some of the barriers included computational issues when generating high-fidelity synthetic data from complex data structures. High-fidelity data are achievable but only in the context of a specific research question and a limited number of variables. Results from the Natsal-3 data showed that parametric methods produced slightly better data utility compared to non-parametric methods. Results for HES and NPD will also be presented.
ConclusionsLow-fidelity synthetic data can provide a useful resource to support users of administrative data, whilst minimising data access timelines and while retaining privacy and confidentiality of personal data. High-utility datasets can be generated but take considerable resources, and current approaches cannot fully handle the complexity of longitudinal administrative data.
Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which do not hold any confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data, describe common methods used to generate synthetic data in the context of UK administrative research, propose a simplified terminology for categories of synthetic data, and illustrate challenges and future directions for research.
