Controlled Generation for Private Synthetic Text
Authors: name: Zihao Zhao; name: Anjalie Field
Published: 2025-09-30 03:38:36

Abstract: 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.