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

2026-04-02

AI Summary

This study addresses the challenge of sharing real-world educational data under strict privacy constraints, where existing differentially private synthetic data methods—often reliant on deep learning—are hindered by engineering complexity and limited practicality in small-sample, high-dimensional settings. The authors propose a training-free, two-stage framework: first, leveraging large language models to generate differentially private synthetic data for broad sharing and exploratory analysis; second, enabling on-demand validation of research findings on the original data through a secure remote code submission mechanism. Evaluated on three years of real educational data, the approach achieves synthetic data quality comparable to deep learning baselines while substantially reducing implementation overhead. Case studies show that approximately 36% of findings are reproducible on the真实 data, with the validation process introducing only negligible additional privacy loss.

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.

Problem

• differentially private synthetic data generation
• educational data sharing
• real-world data
• privacy constraints
• small-sample high-dimensional data

Innovation

• training-free
• LLM-based DP-SDG
• on-demand validation
• educational data sharing
• differential privacy