Summary of Safesynthdp: Leveraging Large Language Models For Privacy-preserving Synthetic Data Generation Using Differential Privacy, by Md Mahadi Hasan Nahid et al.
SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by Md Mahadi Hasan Nahid, Sadid Bin Hasan
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the use of Large Language Models (LLMs) to generate synthetic datasets that preserve privacy while maintaining data utility. The authors propose an approach that integrates Differential Privacy (DP) mechanisms into the data generation process using Laplace and Gaussian distributions. They evaluate the performance of ML models trained on these DP-enhanced synthetic datasets against those trained on original data, finding a viable balance between privacy protection and data utility. The study demonstrates the potential of LLMs to generate synthetic data that satisfies legislative frameworks like GDPR and CCPA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning models safer for personal information. Right now, many ML models are trained on data that includes private information, which raises big privacy concerns. To fix this, some laws, like GDPR and CCPA, require us to find ways to keep data private while still using it to train models. The authors of this paper suggest a new way to do this using Large Language Models (LLMs) and something called Differential Privacy (DP). They show that LLMs can generate fake datasets that are safe and useful for ML model training, without sharing the original private information. |
Keywords
» Artificial intelligence » Machine learning » Synthetic data
