SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data

arXiv:2404.15821v1 Announce Type: new
Abstract: 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. SynthEval, a novel open-source evaluation framework distinguishes itself from existing tools by treating categorical and numerical attributes with equal care, without assuming any special kind of preprocessing steps. This~makes it applicable to virtually any synthetic dataset …

abstract arxiv cs.lg cs.pf data data privacy demand evaluation fairness framework machine machine learning novel privacy risks robust synthetic synthetic data tabular tools type utility