  Authorship obfuscation aims to disguise the identity of an author within a
text by altering the writing style, vocabulary, syntax, and other linguistic
features associated with the text author. This alteration needs to balance
privacy and utility. While strong obfuscation techniques can effectively hide
the author's identity, they often degrade the quality and usefulness of the
text for its intended purpose. Conversely, maintaining high utility tends to
provide insufficient privacy, making it easier for an adversary to de-anonymize
the author. Thus, achieving an optimal trade-off between these two conflicting
objectives is crucial. In this paper, we propose TAROT: Task-Oriented
Authorship Obfuscation Using Policy Optimization, a new unsupervised authorship
obfuscation method whose goal is to optimize the privacy-utility trade-off by
regenerating the entire text considering its downstream utility. Our approach
leverages policy optimization as a fine-tuning paradigm over small language
models in order to rewrite texts by preserving author identity and downstream
task utility. We show that our approach largely reduce the accuracy of
attackers while preserving utility. We make our code and models publicly
available.
