@inproceedings{ma-rajtmajer-2026-private,
title = "Private Seeds, Public {LLM}s: Realistic and Privacy-Preserving Synthetic Data Generation",
author = "Ma, Qian and
Rajtmajer, Sarah",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.10/",
pages = "189--210",
ISBN = "979-8-89176-395-1",
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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%0 Conference Proceedings
%T Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
%A Ma, Qian
%A Rajtmajer, Sarah
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ma-rajtmajer-2026-private
%X 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.
%U https://aclanthology.org/2026.findings-acl.10/
%P 189-210
Markdown (Informal)
[Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation](https://aclanthology.org/2026.findings-acl.10/) (Ma & Rajtmajer, Findings 2026)
ACL