May 14, 2024, 4:11 a.m. | Narasimha Raghavan Veeraragavan, Mohammad Hossein Tabatabaei, Severin Elvatun, Vibeke Binz Vallevik, Siri Lar{\o}nningen, Jan F Nyg{\aa}rd

cs.CR updates on arXiv.org arxiv.org

arXiv:2405.07196v1 Announce Type: cross
Abstract: Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. 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 …

address arxiv bias blockchain challenges cs.cr cs.db cs.dc cs.lg data evaluation framework generator options privacy privacy concerns scarcity select solution synthetic synthetic data

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