Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

• URL: http://arxiv.org/abs/2608.29674v1
• Date: Sun, 30 Aug 2026 09:16:21 GMT
• Title: Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis
• Abstract summary: We introduce Tabular Synthesis Strategy Designer (TabSSD), which uses an LLM to design synthesis procedures rather than directly generate records.<n>Across twelve datasets, TabSSD strikes a favourable balance among statistical fidelity, predictive utility, and empirical privacy risk.<n>It substantially reduces local computation and token consumption relative to the compared methods.
• Score: 36.16910114462388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Differentially Private Synthetic Data via APIs 4: Tabular Data [14.625539540842672]
  State-of-the-art methods often focus on minimizing low-order marginal query errors and overlook the challenges posed by high-order correlations.<n>To address this gap, we extend the Private Evolution framework, originally developed for DP-compliant image and text synthesis.
  arXiv  Detail & Related papers  (2026-06-06T17:08:41Z)
• Concordia: Self-Improving Synthetic Tables for Federated LLMs [80.03837595689608]
  Federated learning enables training large language models (LLMs) without sharing raw data.<n>We propose Concordia, a framework that aligns synthetic data generation with federated validation utility.
  arXiv  Detail & Related papers  (2026-05-11T01:17:58Z)
• DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning [51.35628297101575]
  Differentially private (DP) synthetic data generation plays a pivotal role in developing large language models (LLMs) on private data.<n>We introduce Differentially Private Reinforcement Fine-Tuning (DP-RFT), an online reinforcement learning algorithm for synthetic data generation with LLMs.<n>We evaluate DP-RFT for long-form and domain-specific synthetic data generation, such as news articles, meeting transcripts, and medical article abstracts.
  arXiv  Detail & Related papers  (2026-02-20T22:03:56Z)
• FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs [3.703188184729035]
  Synthetic data generation is an invaluable solution in scenarios where real-world data collection and usage are limited by cost and scarcity.<n>Existing approaches that directly use large language models to generate each record individually impose prohibitive time and cost burdens.<n>We propose a fast, cost-effective method for realistic tabular data synthesis that leverages LLMs to infer and encode each field's distribution into a reusable sampling script.
  arXiv  Detail & Related papers  (2025-07-21T17:51:46Z)
• LLM-Symbolic Integration for Robust Temporal Tabular Reasoning [69.27153114778748]
  We introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations.<n>This structured approach allows Large Language Models (LLMs) to generate and executesql queries, enhancing generalization and mitigating biases.
  arXiv  Detail & Related papers  (2025-06-06T05:14:04Z)
• LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion [49.898152180805454]
  Synthetic datasets must maintain domain-specific logical consistency.<n>Existing generative models often overlook these inter-column relationships.<n>This study presents the first method to effectively preserve inter-column relationships without requiring domain knowledge.
  arXiv  Detail & Related papers  (2025-03-04T00:47:52Z)
• HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection [44.225151701532454]
  In this paper, we introduce a new framework HARMONIC for tabular data generation and evaluation. Our framework achieves equivalent performance to existing methods with better privacy, which also demonstrates our evaluation framework for the effectiveness of synthetic data and privacy risks.
  arXiv  Detail & Related papers  (2024-08-06T03:21:13Z)
• TabuLa: Harnessing Language Models for Tabular Data Synthesis [4.539846270369207]
  Tabula is a tabular data synthesizer that leverages the structure of large language models (LLMs)<n>Unlike state-of-the-art (SOTA) LLMs, Tabula discards the pre-trained weights originally designed for natural language tasks.<n> experiments show that Tabula achieves superior synthetic data utility compared to current SOTA methods.
  arXiv  Detail & Related papers  (2023-10-19T13:50:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.