Training-Free Private Synthesis with Validation: A New Frontier for Practical Educational Data Sharing

TL;DR

This paper introduces a practical, training-free LLM-based differential privacy synthetic data generation method for educational data sharing, enabling easier implementation and validation with moderate privacy risks.

Contribution

It proposes a novel two-stage approach combining training-free LLM-based DP-SDG with on-demand validation, reducing engineering effort for educational data sharing.

Findings

LLM-based DP-SDG performs comparably to deep learning baselines.

The method significantly reduces engineering costs.

Moderate privacy leakage occurs during validation.

Abstract

While secondary use of real-world data (RWD) in education offers substantial research opportunities, data sharing is often limited by privacy constraints. Differentially private synthetic data generation (DP-SDG) has emerged as a possible solution. However, educational RWD is fragmented across platforms and institutions and stored in different formats, so DP-SDG must be tailored to each dataset, requiring substantial engineering effort. In addition, such data are often small-sample and high-dimensional, making deep learning (DL)-based methods common but difficult to implement without specialist expertise. In this setting, it is also hard to achieve practically useful downstream utility. As a result, despite its theoretical promise, DP-SDG remains far from a practical solution in education. To address this issue, we propose a more practical two-stage method: (1) training-free, LLM-based DP-SDG is performed for sharing synthetic data and (2) on-demand real-data validation, where researchers submit code for remote validation of results. This simple method is designed for individual data custodians without extensive DP-SDG expertise. It can also be adapted to multi-shot synthesis, where data from different learner cohorts are synthesised regularly. We evaluate this method experimentally in both the one-shot and multi-shot synthesis settings using RWD collected over three years and conduct a case study with real researchers. Results show that LLM-based DP-SDG performs comparably to a DL-based baseline while greatly reducing engineering costs, and that non-DP validation causes measurable but moderate privacy leakage. Nonetheless, in the case study researchers reported that on average only 36% of synthetic findings are validated on real data. Overall, the paper provides a practical method for sharing educational RWD, while highlighting challenges in risk mitigation and epistemic precision.