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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.04911 [cs.LG] |
| (or arXiv:2605.04911v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04911 arXiv-issued DOI via DataCite (pending registration) |
From: Xinyan Han [view email]
[v1]
Wed, 6 May 2026 13:38:16 UTC (8,236 KB)
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