Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs
Tan, Bowen Xu, Zheng Xing, Eric Hu, Zhiting Wu, Shanshan
Tan, Bowen
Xu, Zheng
Xing, Eric
Hu, Zhiting
Wu, Shanshan
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Synthetic data offers a promising path to train models while preserving data privacy. Differentially private (DP) finetuning of large language models (LLMs) as data generator is effective, but is impractical when computation resources are limited. Meanwhile, prompt-based methods such as private evolution (Xie et al., 2024; Hou et al., 2024) depend heavily on the manual prompts, and ineffectively use private information in their iterative data selection process. To overcome these limitations, we propose CTCL (Data Synthesis with ConTrollability and CLustering), a novel framework for generating privacy-preserving synthetic data without extensive prompt engineering or billion-scale LLM finetuning. CTCL pretrains a lightweight 140M conditional generator and a clustering-based topic model on large-scale public data. To further adapt to the private domain, the generator is DP finetuned on private data for fine-grained textual information, while the topic model extracts a DP histogram representing distributional information. The DP generator then samples according to the DP histogram to synthesize a desired number of data examples. Evaluation across five diverse domains demonstrates the effectiveness of our framework, particularly in the strong privacy regime. Systematic ablation validates the design of each framework component and highlights the scalability of our approach.
Citation
B. Tan, Z. Xu, E. Xing, Z. Hu, and S. Wu, “Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/tan25g.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
Keywords
Subjects
Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press