Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion

Tianyuan Zou, Yang Liu, Peng Li, Yufei Xiong, Jianqing Zhang, Jingjing Liu, Ye Ouyang, Xiaozhou Ye, Yaqin Zhang

Keywords: Differentially Private Synthetic Dataset, Collaboration between Private Data and Private Model, Fusion of Pre-trained Language Model

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 language models (PLM) 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.

Submission Number: 80