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Data sharing is a necessity for innovative progress in many domains,especially in healthcare. However, the ability to share data is hindered byregulations protecting the privacy of natural persons. Synthetic tabular dataprovide a promising solution to address data sharing difficulties but does notinherently guarantee privacy. Still, there is a lack of agreement onappropriate methods for assessing the privacy-preserving capabilities ofsynthetic data, making it difficult to compare results across studies. To thebest of our knowledge, this is the first work to identify properties thatconstitute good universal privacy evaluation metrics for synthetic tabulardata. The goal of such metrics is to enable comparability across studies and toallow non-technical stakeholders to understand how privacy is protected. Weidentify four principles for the assessment of metrics: Comparability,Applicability, Interpretability, and Representativeness (CAIR). To quantify andrank the degree to which evaluation metrics conform to the CAIR principles, wedesign a rubric using a scale of 1-4. Each of the four properties is scored onfour parameters, yielding 16 total dimensions. We study the applicability andusefulness of the CAIR principles and rubric by assessing a selection ofmetrics popular in other studies. The results provide granular insights intothe strengths and weaknesses of existing metrics that not only rank the metricsbut highlight areas of potential improvements. We expect that the CAIRprinciples will foster agreement among researchers and organizations on whichuniversal privacy evaluation metrics are appropriate for synthetic tabulardata.

Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data