Satisfying Disclosure Restrictions With Synthetic Data Sets

By Jerome P. Reiter. 2002

163 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8b2938329330be136efc6448878e02b860ab9dbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “A simulation study of it was given in [60].”
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).”
Significance tests for multi-component estimands from multiply imputed, synthetic microdata (2005, background): “Other variants and discussions of synthetic data approaches appear in Fienberg et al. (1996, 1998), Dandekar et al. (2002a, b), Franconi and Stander (2002, 2003), Polettini et al. (2002), Polettini (2003), and Reiter (2002, 2004).”
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017, background): “Yet, historically, these approaches have been limited to summary statistics for only several variables at a time (e.g., (Dreschsler, 2011; Reiter, 2002).”
Using CART to generate partially synthetic public use microdata (2005, methodology): “For discussions of synthetic approaches, see Fienberg et al. (1996; 1998), Dandekar (2002a; b), and Reiter (2002; 2005a). q Statistics Sweden 1 Institute of Statistics and Decision Sciences, Box 90251, Duke University, Durham, NC 27708-0251, U.S.A. Email: jerry@stat.duke.edu Acknowledgments: This…”
Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study (2005, unknown): “ 2005 Royal Statistical Society 0964–1998/05/168185 J. R. Statist. Soc. A (2005) 168, Part 1, pp.”
Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks (2017, background): “Typically, de-identification is accomplished through the perturbation of potentially identifiable attributes (e.g., dates of birth) via generalization, suppression or randomization (El Emam et al., 2015).”