4 citations · 8 across the 4 of their papers we have counts for
5 papers
Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data
Gillian M Raab
This paper introduces two methods of creating differentially private (DP) synthetic data that are now incorporated into the \textit{synthpop} package for \textbf{R}. Both are suita…
Assessing, visualizing and improving the utility of synthetic data
Gillian M Raab, Beata Nowok, Chris Dibben
The synthpop package for R https://www.synthpop.org.uk provides tools to allow data custodians to create synthetic versions of confidential microdata that can be distributed with f…
Guidelines for Producing Useful Synthetic Data
Gillian M. Raab, Beata Nowok, Chris Dibben
We report on our experiences of helping staff of the Scottish Longitudinal Study to create synthetic extracts that can be released to users. In particular, we focus on how the synt…
General and specific utility measures for synthetic data
Joshua Snoke, Gillian Raab, Beata Nowok +2
Data holders can produce synthetic versions of datasets when concerns about potential disclosure restrict the availability of the original records. This paper is concerned with met…
A simplified approach to generating synthetic data for disclosure control
Gillian Raab, Beata Nowok, Chris Dibben
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 St…