  In the context of information systems, text sanitization techniques are used
to identify and remove sensitive data to comply with security and regulatory
requirements. Even though many methods for privacy preservation have been
proposed, most of them are focused on the detection of entities from specific
domains (e.g., credit card numbers, social security numbers), lacking
generality and requiring customization for each desirable domain. Moreover,
removing words is, in general, a drastic measure, as it can degrade text
coherence and contextual information. Less severe measures include substituting
a word for a safe alternative, yet it can be challenging to automatically find
meaningful substitutions. We present a zero-shot text sanitization technique
that detects and substitutes potentially sensitive information using Large
Language Models. Our evaluation shows that our method excels at protecting
privacy while maintaining text coherence and contextual information, preserving
data utility for downstream tasks.
