Differentially Private Synthetic Data via Foundation Model APIs 2: Text¶
Arxiv Link - 2024-03-04 05:57:50
Abstract¶
Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it. A lot of high-quality text data generated in the real world is private and therefore cannot be shared or used freely due to privacy concerns. Generating synthetic replicas of private text data with a formal privacy guarantee, i.e., differential privacy (DP), offers a promising and scalable solution. However, existing methods necessitate DP finetuning of large language models (LLMs) on private data to generate DP synthetic data. This approach is not viable for proprietary LLMs (e.g., GPT-3.5) and also demands considerable computational resources for open-source LLMs. Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models. In this work, we propose an augmented PE algorithm, named Aug-PE, that applies to the complex setting of text. We use API access to an LLM and generate DP synthetic text without any model training. We conduct comprehensive experiments on three benchmark datasets. Our results demonstrate that Aug-PE produces DP synthetic text that yields competitive utility with the SOTA DP finetuning baselines. This underscores the feasibility of relying solely on API access of LLMs to produce high-quality DP synthetic texts, thereby facilitating more accessible routes to privacy-preserving LLM applications. Our code and data are available at https://github.com/AI-secure/aug-pe.
Socials¶
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| 🌟 Exciting News in the World of AI and Privacy-Preserving Technologies! 🌟 Text data privacy is a crucial concern in the age of machine learning. Check out the groundbreaking work by Lin et al. introducing the Aug-PE algorithm, designed to generate differentially private (DP) synthetic text data using API access to large language models (LLMs) without the need for model training. Curious to learn more about how Aug-PE can revolutionize privacy-preserving LLM applications? Dive into the details and explore the impressive results from comprehensive experiments on benchmark datasets. The study showcases that Aug-PE produces high-quality DP synthetic text comparable to state-of-the-art DP finetuning baselines. Ready to explore the future of privacy-preserving AI applications? Access the code and data at: https://github.com/AI-secure/aug-pe #AI #PrivacyPreservation #MachineLearning #DifferentialPrivacy #AugmentedPE #TechInnovation |
🚀 Exciting new research alert! Aug-PE algorithm enables generating high-quality differentially private synthetic text without model training. Check out the groundbreaking study by Lin et al. (2024) and explore the code and data at: 🔗 http://arxiv.org/abs/2403.01749v1 #AI #NLP #LLMs #PrivacyPreservation #MachineLearning |