General and specific utility measures for synthetic data

• Published in: Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 181; no. 3; pp. 663 - 688
• Main Authors: Snoke, Joshua, Raab, Gillian M., Nowok, Beata, Dibben, Chris, Slavkovic, Aleksandra
• Format: Journal Article
• Language: English
• Published: Oxford Wiley 01.06.2018; Oxford University Press
• Subjects: Classification and regression trees; Computer simulation; Confidence intervals; Disclosure control; Original Articles; Parameter estimation; Privacy; Propensity; Propensity score; Simulation; Synthetic data; Utility; Water flow
• ISSN: 0964-1998; 1467-985X
• DOI: 10.1111/rssa.12358

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.