Differentially private data augmentation via LLM generation with discriminative and distribution-aligned filtering

Data augmentation (DA) is a widely adopted approach for mitigating data insufficiency. Conducting DA in private domains requires privacy-preserving text generation, including anonymization or perturbation applied to sensitive textual data. The above methods lack formal protection guarantees. Existing Differential Privacy (DP) learning methods provide theoretical guarantees by adding calibrated noise to models or outputs. However, the large output space and model scales in text generation require substantial noise, which severely degrades synthesis quality. In this paper, we transfer DP-based synthetic sample generation to DP-based sample discrimination. Specifically, we propose a DP-based DA framework with a large language model (LLM) and a DP-based discriminator for private-domain text generation. Our key idea is to (1) leverage LLMs to generate large-scale high-quality samples, (2) select synthesized samples fitting the private domain, and (3) align the label distribution with the private domain. To achieve this, we use knowledge distillation to construct a DP-based discriminator: teacher models, accessing private data, guide a student model to select samples under calibrated noise. A DP-based tutor further constrains the label distribution of synthesized samples with a low privacy budget. We theoretically analyze the privacy guarantees and empirically validate our method on three medical text classification datasets, showing that our DP-synthesized samples significantly outperform state-of-the-art DP fine-tuning baselines in utility.