Summary of Differentially Private Tabular Data Synthesis Using Large Language Models, by Toan V. Tran and Li Xiong
Differentially Private Tabular Data Synthesis using Large Language Models
by Toan V. Tran, Li Xiong
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DP-LLMTGen, a novel framework for differentially private tabular data synthesis that leverages pretrained large language models (LLMs). The framework uses a two-stage fine-tuning procedure with a novel loss function designed specifically for tabular data. The authors evaluate DP-LLMTGen on multiple datasets and privacy settings, showing it outperforms existing mechanisms. They also conduct an ablation study and experimental analyses to understand LLMs’ role in addressing this problem. Additionally, the framework demonstrates controllable generation through a fairness-constrained setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DP-LLMTGen is a new way to make fake data that’s private. This is important because it lets people share their real data while keeping some information secret. The team created a special tool that uses large language models to generate fake data that looks realistic. They tested this tool on many different sets of data and showed it works better than other methods. They also did extra experiments to learn more about how these big language models work in making private data. |
Keywords
» Artificial intelligence » Fine tuning » Loss function
