Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion

• URL: http://arxiv.org/abs/2502.00245v1
• Date: Sat, 01 Feb 2025 00:54:25 GMT
• Title: Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
• Authors: Tianyuan Zou, Yang Liu, Peng Li, Yufei Xiong, Jianqing Zhang, Jingjing Liu, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang,
• Abstract summary: Existing methods relying on pre-trained models for data synthesis often struggle in data-deficient scenarios. We propose a novel contrAstive private data Synthesis via weighted multiple Pre-trained language models (PLM) framework, named as WASP.
• Score: 16.292666568019577
• License:
• 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 %that avoid fine-tuning large pre-trained generative models 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 contrAstive private data Synthesis via Weighted multiple Pre-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.

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