Controlled Generation for Private Synthetic Text
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.
68df3569df49fb0df1e03caf
Zihao Zhao
claimed_verified
2025-10-03T12:29:53.878Z
6611cb2d80d295a494f08f7e
/avatars/b6095891d4260c625fa727927dac9583.svg
Zihao Zhao
user
zzhao0104
68df3569df49fb0df1e03cb0
Anjalie Field
Controlled Generation for Private Synthetic Text
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.
A novel methodology for privacy-preserving synthetic text generation using entity-aware control codes and HIPS theory achieves a balance between privacy and utility in sensitive domains.
de-identification Hiding In Plain Sight (HIPS) entity-aware control codes in-context learning (ICL) prefix tuning custom masking strategy loss function