A simplified approach to generating synthetic data for disclosure control
Methodology
2017-12-12 v7
Abstract
We describe results on the creation and use of synthetic data that were derived in the context of a project to make synthetic extracts available for users of the UK Longitudinal Studies. A critical review of existing methods of inference from large synthetic data sets is presented. We introduce new variance estimates for use with large samples of completely synthesised data that do not require them to be generated from the posterior predictive distribution derived from the observed data. We make recommendations on how to synthesise data based on these findings. An example of synthesising data from the Scottish Longitudinal Study is included to illustrate our results.
Keywords
Cite
@article{arxiv.1409.0217,
title = {A simplified approach to generating synthetic data for disclosure control},
author = {Gillian Raab and Beata Nowok and Chris Dibben},
journal= {arXiv preprint arXiv:1409.0217},
year = {2017}
}
Comments
This version has minor clarifications of the derivations in section 2