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

Year: 2026

Type: conference-paper

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-theart private synthetic data generation methods demonstrate that RPSG achieves high fidelity to private data while providing strong privacy protection.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.10

Authors: Qian Ma, Sarah Rajtmajer

Institution: Pennsylvania State University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166886659

FWCI:

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Related to: 10