Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
Tianyuan Zou, Yang Liu, Peng Li, Yufei Xiong, Jianqing Zhang, Jingjing Liu, Xiaozhou Ye, Ye Ouyang
Abstract
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias. To address these challenges, we propose a novel contr**A**stive private data **S**ynthesis via **W**eighted multiple **P**re-trained generative models framework, named as **WASP**. WASP utilizes limited private samples for more accurate private data distribution estimation via a Top-*Q* voting mechanism, and leverages low-quality synthetic samples for contrastive generation via collaboration among dynamically weighted multiple pre-trained models. Extensive experiments on 6 well-developed datasets with 6 open-source and 3 closed-source PLMs demonstrate the superiority of WASP in improving model performance over diverse downstream tasks. Code is available at https://github.com/LindaLydia/WASP.
BibTeX
@inproceedings{
zou2025contrastive,
title={Contrastive Private Data Synthesis via Weighted Multi-{PLM} Fusion},
author={Tianyuan Zou and Yang Liu and Peng Li and Yufei Xiong and Jianqing Zhang and Jingjing Liu and Xiaozhou Ye and Ye Ouyang and Ya-Qin Zhang},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=oRdfFS7xO5}
}