title: TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization
    Methods
summary: 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.
paper_authors._id: 66ab2d571f83b210aeafc302
paper_authors.name: Gabriel Loiseau
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-01T15:39:22.775Z
paper_authors._id: 661d6d36a3e99b518d6b9a71
paper_authors.avatarUrl: /avatars/4135e3ce0ae64dc62ccabf1e534f98bd.svg
paper_authors.fullname: Gabriel Loiseau
paper_authors.type: user
paper_authors.user: gabrielloiseau
paper_authors._id: 66ab2d571f83b210aeafc303
paper_authors.name: Damien Sileo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T15:28:31.818Z
paper_authors._id: 5fc0bcb41160c47d1d43856b
paper_authors.avatarUrl: /avatars/4d68767d363f7bc5605906c0e994f132.svg
paper_authors.fullname: Damien Sileo
paper_authors.type: user
paper_authors.user: sileod
paper_authors._id: 66ab2d571f83b210aeafc304
paper_authors.name: Damien Riquet
paper_authors._id: 66ab2d571f83b210aeafc305
paper_authors.name: Maxime Meyer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-02T15:28:45.273Z
paper_authors._id: 64d40b3465aabf1f6b6f3dbe
paper_authors.avatarUrl: /avatars/abf4a37393d316f7469586d38a9b3005.svg
paper_authors.fullname: Maxime Meyer
paper_authors.type: user
paper_authors.user: Max160
paper_authors._id: 66ab2d571f83b210aeafc306
paper_authors.name: Marc Tommasi
paper_title: TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization
    Methods
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: unsupervised authorship obfuscation policy optimization language models task utility de-anonymization