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arXiv · 2205.05993

On integrating the number of synthetic data sets $m$ into the 'a priori' synthesis approach

James Edward Jackson·Robin Mitra·Brian Joseph Francis·Iain Dove
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Abstract

Until recently, multiple synthetic data sets were always released to analysts, to allow valid inferences to be obtained. However, under certain conditions - including when saturated count models are used to synthesize categorical data - single imputation ($m=1$) is sufficient. Nevertheless, increasing $m$ causes utility to improve, but at the expense of higher risk, an example of the risk-utility trade-off. The question, therefore, is: which value of $m$ is optimal with respect to the risk-utility trade-off? Moreover, the paper considers two ways of analysing categorical data sets: as they have a contingency table representation, multiple categorical data sets can be averaged before being analysed, as opposed to the usual way of averaging post-analysis. This paper also introduces a pair of metrics, $τ_3(k,d)$ and $τ_4(k,d)$, that are suited for assessing disclosure risk in multiple categorical synthetic data sets. Finally, the synthesis methods are demonstrated empirically.

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James Edward Jackson, Robin Mitra, Brian Joseph Francis, Iain Dove. 2022-05-12. On integrating the number of synthetic data sets $m$ into the 'a priori' synthesis approach. https://arxiv.org/abs/2205.05993

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