Using CART to generate partially synthetic public use microdata

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Synthesizing Tabular Data using Generative Adversarial Networks (2018, methodology): “There are several statistical ways to generate this kind of synthetic data, including classiﬁcation and regression trees [41, 29] and Bayesian networks [12, 30, 43].”
Modeling Tabular data using Conditional GAN (2019, methodology): “For example, a set of discrete variables may have been modeled using decision trees [20] and Bayesian networks [2, 28].”
Privacy: Theory meets Practice on the Map (2008, background): “Subsequently, many techniques have been proposed for generating synthetic data [30], [37], [38] and creating statistical inferences from them [2], [3].”
A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “Methods for valid inference on this kind of partial synthetic data were developed in [61] and a non-parametric method was presented in [62] to generate multiply-imputed, partially synthetic data.”
synthpop: Bespoke Creation of Synthetic Data in R (2016, unknown): “They have been discussed and exempliﬁed in a further series of papers (Abowd and Lane 2004; Abowd and Woodcock 2004; Reiter 2002, 2005a; Drechsler and Reiter 2010; Kinney, Reiter, and Berger 2010; Kinney, Reiter, Reznek, Miranda, Jarmin, and Abowd 2011).”