  Authorship obfuscation techniques hold the promise of helping people protect
their privacy in online communications by automatically rewriting text to hide
the identity of the original author. However, obfuscation has been evaluated in
narrow settings in the NLP literature and has primarily been addressed with
superficial edit operations that can lead to unnatural outputs. In this work,
we introduce an automatic text privatization framework that fine-tunes a large
language model via reinforcement learning to produce rewrites that balance
soundness, sense, and privacy. We evaluate it extensively on a large-scale test
set of English Reddit posts by 68k authors composed of short-medium length
texts. We study how the performance changes among evaluative conditions
including authorial profile length and authorship detection strategy. Our
method maintains high text quality according to both automated metrics and
human evaluation, and successfully evades several automated authorship attacks.
