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arXiv · 2604.07486

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation

Qian Ma·Sarah Rajtmajer
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Abstract

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.

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Qian Ma, Sarah Rajtmajer. 2026-04-08. Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation. https://doi.org/10.18653/v1%2F2026.findings-acl.10

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