Differentially Private Synthetic Data via Foundation Model APIs 2: Text

arXiv:2403.01749v1 Announce Type: new
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 …

apis arxiv cs.cl data foundation foundation model synthetic synthetic data text type via