AI RESEARCH
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
arXiv CS.LG
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ArXi:2605.04911v1 Announce Type: new Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-data regime: improving data quality typically comes at the cost of increased memorization of