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

• URL: http://arxiv.org/abs/2604.07486v2
• Date: Sat, 11 Apr 2026 17:27:44 GMT
• Title: Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
• Abstract summary: We propose Realistic and Privacy-Preserving Synthetic Data Generation (RPSG), which uses private seeds and integrates privacy-preserving strategies.<n>RPSG achieves high fidelity to private data while providing strong privacy protection.
• Score: 6.645945056204045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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