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Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators
In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a specific purpose?
Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare
We present a conceptual framework for quality assurance of SD for AI applications in healthcare that aligns diverging taxonomies, expands on common quality dimensions to include the dimensions of Fairness and Carbon footprint, and proposes stages necessary to support real-life applications.
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