TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization
  Methods
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.
66ab2d571f83b210aeafc302
Gabriel Loiseau
claimed_verified
2024-08-01T15:39:22.775Z
661d6d36a3e99b518d6b9a71
/avatars/4135e3ce0ae64dc62ccabf1e534f98bd.svg
Gabriel Loiseau
user
gabrielloiseau
66ab2d571f83b210aeafc303
Damien Sileo
admin_assigned
2024-08-02T15:28:31.818Z
5fc0bcb41160c47d1d43856b
https://cdn-avatars.huggingface.co/v1/production/uploads/5fc0bcb41160c47d1d43856b/AHCEW4TfTdyjNBx-V_F5A.png
Damien Sileo
user
sileod
66ab2d571f83b210aeafc304
Damien Riquet
66ab2d571f83b210aeafc305
Maxime Meyer
admin_assigned
2024-08-02T15:28:45.273Z
64d40b3465aabf1f6b6f3dbe
/avatars/abf4a37393d316f7469586d38a9b3005.svg
Maxime Meyer
user
Max160
66ab2d571f83b210aeafc306
Marc Tommasi
TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization
  Methods
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.
TAROT optimizes the privacy-utility trade-off in authorship obfuscation using policy optimization over small language models to preserve task utility while reducing de-anonymization accuracy.
unsupervised authorship obfuscation policy optimization language models task utility de-anonymization