Large language models (LLMs) have emerged as a powerful tool for synthetic data generation. A particularly important use case is producing synthetic replicas of private text, which requires carefully balancing privacy and utility. We propose Realistic and Privacy-Preserving Synthetic Data Generation (RPSG), which uses private seeds and integrates privacy-preserving strategies, including a formal differential privacy (DP) mechanism in the candidate selection, to generate realistic synthetic data. Comprehensive experiments against state-of-the-art private synthetic data generation methods demonstrate that RPSG achieves high fidelity to private data while providing strong privacy protection.
@article{arxiv.2604.07486,
title = {Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation},
author = {Qian Ma and Sarah Rajtmajer},
journal= {arXiv preprint arXiv:2604.07486},
year = {2026}
}