Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data

Understand

Recent advances in generating synthetic data that allow to add principled ways of protecting privacy -- such as Differential Privacy -- are a crucial step in sharing statistical information in a privacy preserving way.

• But while the focus has been on privacy guarantees, the resulting private synthetic data is only useful if it still carries statistical information from the original data.
• To further optimise the inherent trade-off between data privacy and data quality, it is necessary to think closely about the latter.
• What is it that data analysts want? Acknowledging that data quality is a subjective concept, we develop a framework to evaluate the quality of differentially private synthetic data from an applied researcher's perspective.