title: Trusted Machine Learning Models Unlock Private Inference for Problems
    Currently Infeasible with Cryptography
summary: We often interact with untrusted parties. Prioritization of privacy can limit
  the effectiveness of these interactions, as achieving certain goals
  necessitates sharing private data. Traditionally, addressing this challenge has
  involved either seeking trusted intermediaries or constructing cryptographic
  protocols that restrict how much data is revealed, such as multi-party
  computations or zero-knowledge proofs. While significant advances have been
  made in scaling cryptographic approaches, they remain limited in terms of the
  size and complexity of applications they can be used for. In this paper, we
  argue that capable machine learning models can fulfill the role of a trusted
  third party, thus enabling secure computations for applications that were
  previously infeasible. In particular, we describe Trusted Capable Model
  Environments (TCMEs) as an alternative approach for scaling secure computation,
  where capable machine learning model(s) interact under input/output
  constraints, with explicit information flow control and explicit statelessness.
  This approach aims to achieve a balance between privacy and computational
  efficiency, enabling private inference where classical cryptographic solutions
  are currently infeasible. We describe a number of use cases that are enabled by
  TCME, and show that even some simple classic cryptographic problems can already
  be solved with TCME. Finally, we outline current limitations and discuss the
  path forward in implementing them.
paper_authors._id: 6788d316f2e4691811b58fd0
paper_authors.name: Ilia Shumailov
paper_authors._id: 6788d316f2e4691811b58fd1
paper_authors.name: Daniel Ramage
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T09:41:48.984Z
paper_authors._id: 643c627626f177a3e41912e9
paper_authors.avatarUrl: /avatars/472bbef2630458773cb76bf0d44f0028.svg
paper_authors.fullname: Daniel Ramagem
paper_authors.type: user
paper_authors.user: danrama
paper_authors._id: 6788d316f2e4691811b58fd2
paper_authors.name: Sarah Meiklejohn
paper_authors._id: 6788d316f2e4691811b58fd3
paper_authors.name: Peter Kairouz
paper_authors._id: 6788d316f2e4691811b58fd4
paper_authors.name: Florian Hartmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T09:42:04.838Z
paper_authors._id: 6728eb13efafaef60cff09a5
paper_authors.avatarUrl: /avatars/2effe4ae7f0d0707accfcc308a11c3a3.svg
paper_authors.fullname: Florian Hartmann
paper_authors.type: user
paper_authors.user: fhartmann
paper_authors._id: 6788d316f2e4691811b58fd5
paper_authors.name: Borja Balle
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T09:42:21.854Z
paper_authors._id: 668298c155ecd182e4b78afb
paper_authors.avatarUrl: /avatars/f08100666c609ae1ef5fc50504feacf4.svg
paper_authors.fullname: Borja Balle
paper_authors.type: user
paper_authors.user: bballe
paper_authors._id: 6788d316f2e4691811b58fd6
paper_authors.name: Eugene Bagdasarian
paper_title: Trusted Machine Learning Models Unlock Private Inference for Problems
    Currently Infeasible with Cryptography
paper_summary: We often interact with untrusted parties. Prioritization of privacy can limit
  the effectiveness of these interactions, as achieving certain goals
  necessitates sharing private data. Traditionally, addressing this challenge has
  involved either seeking trusted intermediaries or constructing cryptographic
  protocols that restrict how much data is revealed, such as multi-party
  computations or zero-knowledge proofs. While significant advances have been
  made in scaling cryptographic approaches, they remain limited in terms of the
  size and complexity of applications they can be used for. In this paper, we
  argue that capable machine learning models can fulfill the role of a trusted
  third party, thus enabling secure computations for applications that were
  previously infeasible. In particular, we describe Trusted Capable Model
  Environments (TCMEs) as an alternative approach for scaling secure computation,
  where capable machine learning model(s) interact under input/output
  constraints, with explicit information flow control and explicit statelessness.
  This approach aims to achieve a balance between privacy and computational
  efficiency, enabling private inference where classical cryptographic solutions
  are currently infeasible. We describe a number of use cases that are enabled by
  TCME, and show that even some simple classic cryptographic problems can already
  be solved with TCME. Finally, we outline current limitations and discuss the
  path forward in implementing them.
paper_ai_keywords: Trusted Capable Model Environments (TCMEs) capable machine learning model(s) input/output constraints explicit information flow control statelessness private inference classical cryptographic solutions