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Found 2 papers, 1 papers with code
SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
With the growing demand for synthetic data to address contemporary issues in machine learning, such as data scarcity, data fairness, and data privacy, having robust tools for assessing the utility and potential privacy risks of such data becomes crucial.
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
We study the applicability and usefulness of the CAIR principles and rubric by assessing a selection of metrics popular in other studies.
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