Back to Paper
cs.CRcs.AI
Local ID: 2604.07486v2
AI Summary: gemma4:e4b
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
By Qian Ma, Sarah Rajtmajer
Revision History Timeline
v14/8/2026
4/8/2026
“23 pages, 7 figures, 18 tables”
v24/11/2026
4/11/2026
“22 pages, 7 figures, 18 tables”
★ Version indexed in Explorerv37/13/2026
7/13/2026
“Published in Findings of the Association for Computational Linguistics: ACL 2026. Camera-ready version”
Comparing v2 vs v3
Green = Added • Red = Removed
Title Comparison
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
Authors Comparison
No author changes.
v2 Comment
“22 pages, 7 figures, 18 tables”
v3 Comment
“Published in Findings of the Association for Computational Linguistics: ACL 2026. Camera-ready version”
Abstract Word Diff
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.