Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning

• URL: http://arxiv.org/abs/2605.04911v1
• Date: Wed, 06 May 2026 13:38:16 GMT
• Title: Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
• Abstract summary: We propose DiffICL, which formulates data generation as an in-context learning problem.<n>We evaluate DiffICL on 14 real-world datasets.
• Score: 23.47902798441484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 training samples, thereby weakening privacy protection. This tradeoff arises because small training sets make it difficult for dataset-specific generative models to distinguish generalizable structure from sample-specific patterns. To address this, we propose DiffICL, which formulates tabular data generation as an in-context learning problem. Instead of fitting each dataset from scratch,DiffICL leverages pretrained structural priors learned from a large collection of datasets, enabling it to infer data distributions from limited context rather than memorizing individual samples. We evaluate DiffICL on 14 real-world datasets. Results show that DiffICL improves both data quality and privacy, and generate synthetic data that provides effective data augmentation. Our findings suggest that the quality-privacy tradeoff can be improved through better training paradigms.

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