Noise-Aware Statistical Inference with Differentially Private Synthetic Data. (arXiv:2205.14485v1 [stat.ML])

While generation of synthetic data under differential privacy (DP) has
received a lot of attention in the data privacy community, analysis of
synthetic data has received much less. Existing work has shown that simply
analysing DP synthetic data as if it were real does not produce valid
inferences of population-level quantities. For example, confidence intervals
become too narrow, which we demonstrate with a simple experiment. We tackle
this problem by combining synthetic data analysis techniques from the field of
multiple …

data ml synthetic data