Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data

• URL: http://arxiv.org/abs/2602.02766v1
• Date: Mon, 02 Feb 2026 20:19:52 GMT
• Title: Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
• Abstract summary: PATH is a novel generative framework that treats the full table as the unit of synthesis.<n>We introduce PATH, a novel generative framework that treats the full table as the unit of synthesis.
• Score: 12.092250204769181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual. This perspective neglects the temporal complexity in longitudinal datasets, such as electronic health records, where a user contributes an entire (sub) table of sequential events. While practitioners might attempt to model such data by flattening user histories into high-dimensional vectors for use with standard marginal-based mechanisms, we demonstrate that this strategy is insufficient. Flattening fails to preserve temporal coherence even when it maintains valid marginal distributions. We introduce PATH, a novel generative framework that treats the full table as the unit of synthesis and leverages the autoregressive capabilities of privately fine-tuned large language models. Extensive evaluations show that PATH effectively captures long-range dependencies that traditional methods miss. Empirically, our method reduces the distributional distance to real trajectories by over 60% and reduces state transition errors by nearly 50% compared to leading marginal mechanisms while achieving similar marginal fidelity.

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