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://anonymous.4open.science/r/WASP.