Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie Zinan Lin Arturs Backurs Sivakanth Gopi Da Yu Huseyin A Inan Harsha Nori Haotian Jiang Huishuai Zhang Yin Tat Lee Bo Li Sergey Yekhanin
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.