Confidential Computing topic
Confidential Computing is the protection of data in use by performing computation in a hardware-based, attested Trusted Execution Environment. A Trusted Execution Environment (TEE) is an environment that provides a level of assurance of the following three properties: data integrity, data confidentiality, and code integrity. TEEs may have additional attributes such as code confidentiality, programmability, recoverability, and attestability. Confidential Computing aims to reduce the ability for the owner/operator/pwner of a platform to access data and code inside TEEs sufficiently such that this path is not an economically or logically viable attack during execution.
tf-encrypted
A Framework for Encrypted Machine Learning in TensorFlow
confidential-computing-zoo
Confidential Computing Zoo provides confidential computing solutions based on Intel SGX, TDX, HEXL, etc. technologies.
enarx.github.io
Enarx.dev website and relevant assets
MP-SPDZ
Versatile framework for multi-party computation
cape-js
The Cape Privacy JavaScript SDK
functions
Sample functions for Cape Privacy
nitrogen
Nitrogen is a tool for deploying web services to AWS Nitro Enclaves.
pycape
The Cape Privacy Python SDK
tf-trusted
tf-trusted allows you to run TensorFlow models in secure enclaves