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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.00245 |
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| _version_ | 1866909471903580160 |
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| author | Zou, Tianyuan Liu, Yang Li, Peng Xiong, Yufei Zhang, Jianqing Liu, Jingjing Ye, Xiaozhou Ouyang, Ye Zhang, Ya-Qin |
| author_facet | Zou, Tianyuan Liu, Yang Li, Peng Xiong, Yufei Zhang, Jianqing Liu, Jingjing Ye, Xiaozhou Ouyang, Ye Zhang, Ya-Qin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion Zou, Tianyuan Liu, Yang Li, Peng Xiong, Yufei Zhang, Jianqing Liu, Jingjing Ye, Xiaozhou Ouyang, Ye Zhang, Ya-Qin Machine Learning 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. |
| title | Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.00245 |