Controlled Generation for Private Synthetic Text

Zihao Zhao, Anjalie Field

Controlled Generation for Private Synthetic Text: 11 upvotes on Hugging Face Daily Papers, #45 of 52 papers on 2025-10-03. Day-by-day upvote history.

Text anonymization is essential for responsibly developing and deploying AI in high-stakes domains such as healthcare, social services, and law. In this work, we propose a novel methodology for privacy-preserving synthetic text generation that leverages the principles of de-identification and the Hiding In Plain Sight (HIPS) theory. Our approach introduces entity-aware control codes to guide controllable generation using either in-context learning (ICL) or prefix tuning. The ICL variant ensures privacy levels consistent with the underlying de-identification system, while the prefix tuning variant incorporates a custom masking strategy and loss function to support scalable, high-quality generation. Experiments on legal and clinical datasets demonstrate that our method achieves a strong balance between privacy protection and utility, offering a practical and effective solution for synthetic text generation in sensitive domains.

Paper page on Hugging Face · arXiv

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