title: Operationalizing Contextual Integrity in Privacy-Conscious Assistants
summary: Advanced AI assistants combine frontier LLMs and tool access to autonomously
  perform complex tasks on behalf of users. While the helpfulness of such
  assistants can increase dramatically with access to user information including
  emails and documents, this raises privacy concerns about assistants sharing
  inappropriate information with third parties without user supervision. To steer
  information-sharing assistants to behave in accordance with privacy
  expectations, we propose to operationalize contextual integrity
  (CI), a framework that equates privacy with the appropriate flow of information
  in a given context. In particular, we design and evaluate a number of
  strategies to steer assistants' information-sharing actions to be CI compliant.
  Our evaluation is based on a novel form filling benchmark composed of synthetic
  data and human annotations, and it reveals that prompting frontier LLMs to
  perform CI-based reasoning yields strong results.
paper_authors._id: 66b1e42e874d8abe0b6e5963
paper_authors.name: Sahra Ghalebikesabi
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paper_authors.fullname: Sahra Ghalebikesabi
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paper_authors._id: 66b1e42e874d8abe0b6e5964
paper_authors.name: Eugene Bagdasaryan
paper_authors.status: admin_assigned
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paper_authors.fullname: Eugene Bagdasaryan
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paper_authors.user: ebagdasa
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paper_authors.name: Ren Yi
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paper_authors.name: Itay Yona
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paper_authors.fullname: Itay Yona
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paper_authors.user: tux
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paper_authors.name: Ilia Shumailov
paper_authors._id: 66b1e42e874d8abe0b6e5968
paper_authors.name: Aneesh Pappu
paper_authors._id: 66b1e42e874d8abe0b6e5969
paper_authors.name: Chongyang Shi
paper_authors._id: 66b1e42e874d8abe0b6e596a
paper_authors.name: Laura Weidinger
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paper_authors.fullname: Laura Weidinger
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paper_authors.user: lweidinger
paper_authors._id: 66b1e42e874d8abe0b6e596b
paper_authors.name: Robert Stanforth
paper_authors._id: 66b1e42e874d8abe0b6e596c
paper_authors.name: Leonard Berrada
paper_authors.status: admin_assigned
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paper_authors._id: 65fc77aad6206abe6cb611f1
paper_authors.avatarUrl: /avatars/306334124d90d1b4d4f82fb47f56156f.svg
paper_authors.fullname: Leonard Berrada
paper_authors.type: user
paper_authors.user: lberrada
paper_authors._id: 66b1e42e874d8abe0b6e596d
paper_authors.name: Pushmeet Kohli
paper_authors._id: 66b1e42e874d8abe0b6e596e
paper_authors.name: Po-Sen Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T11:56:32.948Z
paper_authors._id: 6642957fd55cdaaa95c9d49b
paper_authors.avatarUrl: /avatars/3854266cc3fe72f068693a700dbb287e.svg
paper_authors.fullname: PH
paper_authors.type: user
paper_authors.user: posenhuang
paper_authors._id: 66b1e42e874d8abe0b6e596f
paper_authors.name: Borja Balle
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T11:56:39.575Z
paper_authors._id: 668298c155ecd182e4b78afb
paper_authors.avatarUrl: /avatars/f08100666c609ae1ef5fc50504feacf4.svg
paper_authors.fullname: Borja Balle
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paper_authors.user: bballe
paper_title: Operationalizing Contextual Integrity in Privacy-Conscious Assistants
paper_summary: Advanced AI assistants combine frontier LLMs and tool access to autonomously
  perform complex tasks on behalf of users. While the helpfulness of such
  assistants can increase dramatically with access to user information including
  emails and documents, this raises privacy concerns about assistants sharing
  inappropriate information with third parties without user supervision. To steer
  information-sharing assistants to behave in accordance with privacy
  expectations, we propose to operationalize contextual integrity
  (CI), a framework that equates privacy with the appropriate flow of information
  in a given context. In particular, we design and evaluate a number of
  strategies to steer assistants' information-sharing actions to be CI compliant.
  Our evaluation is based on a novel form filling benchmark composed of synthetic
  data and human annotations, and it reveals that prompting frontier LLMs to
  perform CI-based reasoning yields strong results.
paper_ai_keywords: LLMs (Large Language Models) contextual integrity (CI) privacy information-sharing form filling benchmark synthetic data human annotations CI-based reasoning