Summary of Dp-2stage: Adapting Language Models As Differentially Private Tabular Data Generators, by Tejumade Afonja et al.
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
by Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche, Mario Fritz
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the challenge of generating tabular data under differential privacy (DP) constraints, which ensures theoretical privacy guarantees but hinders machine learning model training. Pre-trained Large Language Models (LLMs) like GPT-2 have shown promise in synthesizing tabular data, but their applications under DP remain unexplored. The authors address this gap by applying DP techniques to synthetic tabular data generation and propose a two-stage fine-tuning framework, ours, which involves non-private fine-tuning on a pseudo dataset followed by DP fine-tuning on a private dataset. Experimental results demonstrate improved performance across various settings and metrics compared to directly fine-tuned LLMs in DP contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generating synthetic tabular data under differential privacy (DP) is important for theoretical privacy guarantees, but it’s hard because machine learning models need complex structures and noisy supervision signals. Some pre-trained language models like GPT-2 can make good synthetic data, but they haven’t been used much with DP. The authors of this paper want to change that by using DP techniques to make synthetic tabular data. They came up with a new way to fine-tune these language models, called ours, which has two stages: first, it gets trained on fake data without worrying about privacy, and then it gets trained again on real private data while keeping the information safe. This makes the results better than just training the model directly in the DP setting. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Machine learning » Synthetic data
