Sharing valuable text data like medical records or legal documents often comes with privacy risks. How can we remove identifying information while preserving the data's usefulness? A new research paper proposes an innovative sanitization approach called INTACT that leverages the power of large language models (LLMs). Instead of simply deleting sensitive information or replacing it with generic labels, INTACT uses LLMs to generate more abstract, yet still truthful, alternatives. Imagine replacing a specific hospital name with "a medical facility" or a birthdate with "spring 1999." This preserves valuable context while making re-identification harder. But how do we ensure these replacements are truly private? INTACT’s clever second stage uses the same LLM to launch simulated "inference attacks." It tries to guess the original information based on the sanitized text. If the LLM succeeds, the replacement is deemed too risky, and a more general alternative is selected. This iterative process balances privacy with utility, ensuring the sanitized text retains as much meaning as possible. Tested on a benchmark dataset of court cases, INTACT outperformed traditional methods like suppression and generic entity replacement. It preserved more of the original document's meaning while posing a lower re-identification risk. This new technique shows great promise for protecting privacy while enabling data sharing for research and other beneficial purposes. However, challenges remain, particularly with ensuring grammatical correctness and consistent abstraction levels in the generated replacements. Future research will focus on refining these aspects, potentially involving additional LLM steps for quality control. This innovative approach represents an exciting step forward in balancing the needs of data privacy and data utility.