用大模型设计数据生成策略，保护隐私且高效。
Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis
- 让大模型设计生成流程而非直接造数据
- 12个数据集上综合表现最佳，计算量更低
- 适合需要透明、可审计合成数据的从业者
在高风险领域共享表格数据受隐私法规制约。合成数据是潜在替代方案，但深度生成模型训练成本高且难审计，基于大模型的方法常将记录序列化为文本，破坏表格结构并暴露敏感信息。本文提出表格数据合成策略设计者（TabSSD），利用大模型设计合成流程而非直接生成记录。TabSSD向大模型提供变量依赖关系的树形摘要，而非原始记录，生成可在本地执行与评估的Python程序。在十二个数据集上，TabSSD在统计保真度、预测效用和经验隐私风险之间取得良好平衡，在六项指标中平均排名最优，优于十种方法。此外，其本地计算开销和令牌消耗显著低于对比方法。通过支持人工引导优化并消除用户端模型调参，降低了透明表格数据合成的技术与基础设施门槛。
原文摘要 · Abstract (English)
Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative models are costly to train and difficult to audit, while LLM-based methods often serialize records as text, obscuring tabular structure and exposing sensitive data. We introduce Tabular Synthesis Strategy Designer (TabSSD), which uses an LLM to design synthesis procedures rather than directly generate records. TabSSD provides the LLM with tree-derived summaries of variable dependence rather than raw records, which produces Python programs for local execution and evaluation. Across twelve datasets, TabSSD strikes a favourable balance among statistical fidelity, predictive utility, and empirical privacy risk, achieving the best average rank across six metrics among ten methods. Moreover, it substantially reduces local computation and token consumption relative to the compared methods. By enabling human-guided refinement and eliminating user-side model tuning, TabSSD lowers the expertise and infrastructure barriers to transparent tabular data synthesis.
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