Trusted Machine Learning Models Unlock Private Inference for Problems
  Currently Infeasible with Cryptography
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.
6788d316f2e4691811b58fd0
Ilia Shumailov
6788d316f2e4691811b58fd1
Daniel Ramage
admin_assigned
2025-01-16T09:41:48.984Z
643c627626f177a3e41912e9
/avatars/472bbef2630458773cb76bf0d44f0028.svg
Daniel Ramagem
user
danrama
6788d316f2e4691811b58fd2
Sarah Meiklejohn
6788d316f2e4691811b58fd3
Peter Kairouz
6788d316f2e4691811b58fd4
Florian Hartmann
admin_assigned
2025-01-16T09:42:04.838Z
6728eb13efafaef60cff09a5
/avatars/2effe4ae7f0d0707accfcc308a11c3a3.svg
Florian Hartmann
user
fhartmann
6788d316f2e4691811b58fd5
Borja Balle
admin_assigned
2025-01-16T09:42:21.854Z
668298c155ecd182e4b78afb
/avatars/f08100666c609ae1ef5fc50504feacf4.svg
Borja Balle
user
bballe
6788d316f2e4691811b58fd6
Eugene Bagdasarian
Trusted Machine Learning Models Unlock Private Inference for Problems
  Currently Infeasible with Cryptography
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.
Trusted Capable Model Environments enable secure machine learning computations for privacy-preserving applications that are currently infeasible with traditional cryptographic methods.
machine learning models Trusted Capable Model Environments (TCMEs) secure computations privacy input/output constraints information flow control statelessness private inference cryptographic problems