1 citations · 3 across the 6 of their papers we have counts for
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stat.ME2023★ 1 cited
Advancing Microdata Privacy Protection: A Review of Synthetic Data
Jingchen Hu, Claire McKay Bowen
Synthetic data generation is a powerful tool for privacy protection when considering public release of record-level data files. Initially proposed about three decades ago, it has g…
stat.ME2021
Bayesian Estimation of Attribute Disclosure Risks in Synthetic Data with the R Package
Ryan Hornby, Jingchen Hu
Synthetic data is a promising approach to privacy protection in many contexts. A Bayesian synthesis model, also known as a synthesizer, simulates synthetic values of sensitive vari…
stat.ME2020
Identification Risks Evaluation of Partially Synthetic Data with the R Package
Ryan Hornby, Jingchen Hu
We extend a general approach to evaluating identification risk of synthesized variables in partially synthetic data. For multiple continuous synthesized variables, we introduce the…