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Recent advances in generating synthetic data that allow to add principledways of protecting privacy -- such as Differential Privacy -- are a crucialstep in sharing statistical information in a privacy preserving way. But whilethe focus has been on privacy guarantees, the resulting private synthetic datais only useful if it still carries statistical information from the originaldata. To further optimise the inherent trade-off between data privacy and dataquality, it is necessary to think closely about the latter. What is it thatdata analysts want? Acknowledging that data quality is a subjective concept, wedevelop a framework to evaluate the quality of differentially private syntheticdata from an applied researcher's perspective. Data quality can be measuredalong two dimensions. First, quality of synthetic data can be evaluated againsttraining data or against an underlying population. Second, the quality ofsynthetic data depends on general similarity of distributions or specific taskssuch as inference or prediction. It is clear that accommodating all goals atonce is a formidable challenge. We invite the academic community to jointlyadvance the privacy-quality frontier.

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