  In this work, we address the problem of text anonymization where the goal is
to prevent adversaries from correctly inferring private attributes of the
author, while keeping the text utility, i.e., meaning and semantics. We propose
IncogniText, a technique that anonymizes the text to mislead a potential
adversary into predicting a wrong private attribute value. Our empirical
evaluation shows a reduction of private attribute leakage by more than 90%.
Finally, we demonstrate the maturity of IncogniText for real-world applications
by distilling its anonymization capability into a set of LoRA parameters
associated with an on-device model.
