Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study

By Jerome P. Reiter. 2005

261 citations recorded by the source corpus.

Subfields: Health, Management Science and Operations Research, Sociology and Political Science

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Citations in the corpus

Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “At the same time, some researchers in the statistical sciences community have begun been using randomization-based methods to generate synthetic data [40, 37, 39, 25].”
An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2017, methodology): “This approach of creating multiple datasets is also used in the privacy settings [Reiter, 2005] and multiple imputation [Rubin, 2004, Reiter and Raghunathan, 2007], where a common default value is M = 10 [Buuren and Groothuis-Oudshoorn, 2011].”
Secure, Privacy-Preserving Analysis of Distributed Databases (2007, methodology): “possible to use the secure data integration algorithm of §3.1, together with methods for constructing (privacypreserving) synthetic residuals in ordinary regressions ( Reiter, 2005 ), to create secure synthetic residuals (Karr et al., 2005b).”
Significance tests for multi-component estimands from multiply imputed, synthetic microdata (2005, background): “Other attractive features of synthetic data are described by Rubin (1993), Little (1993), Raghunathan et al. (2003),and Reiter (2003,2004) .”
A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “This rules out using the method for continuous data.”
Synthetic Data for Deep Learning (2019, background): “Since the main emphasis of the present survey is on synthetic data for deep learning, we do not go into details about multiple imputation and refer to the book [156] and the main recent sources in the field [155,476,483,485].”
A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality (2006, background): “…has been much work on developing measures of disclosure risk (e.g., Duncan and Lambert 1986, 1989; Lambert 1993; Fienberg, Makov, and Sanil 1997; Skinner and Elliot 2002; Reiter 2005a), there has been comparatively little work on developing measures of data utility, and so this article outlines…”
A nonparametric method to generate synthetic populations to adjust for complex sampling design features (2014, background): “…Little 1998; Elliott and Little 2000; Elliott 2007; Chen, Elliott and Little 2010), disclosure risk settings (Little 1993; Raghunathan, Reiter and Rubin 2003; Reiter 2004, 2005), or combining data from multiple surveys (Raghunathan, Xie, Schenker, Parsons, Davis, Dodd and Feuer 2007; Dong 2012).”
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation (2005, background): “Other authors (e.g., Winkler (2004) and Reiter (2004) remark that, at a closer look, even synthetic data might contain some records allowing for re-identification of confidential information.”
Using CART to generate partially synthetic public use microdata (2005, methodology): “Using all units with Ij ¼ 1 can result in biased estimates or wider confidence intervals with overly conservative coverage rates, as illustrated in the simulations of Reiter (2003).”
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).”