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.
A unified framework for privacy-preserving data analysis and machine learning
A Framework for Encrypted Machine Learning in TensorFlow
MPyC: Multiparty Computation in Python
Calculate AMD SEV/SEV-ES/SEV-SNP measurement for confidential computing
Unified API to Access TCG Compliant measurement, event log, quote in Confidential Computing Environment.
The Cape Privacy Python SDK
Sample functions for Cape Privacy
🚧 SDK for building verifiable AI Agents on Flare using Confidential Space
A platform that enables users to perform private benchmarking of machine learning models. The platform facilitates the evaluation of models based on different trust levels between the model owners and the dataset owners.
Deploy the SC2 system on Kubernetes.
a sdk tool to access capsule manager
Example code that leverages Attested-TLS protocol (GENXT confido lib) to verify and access Confidential LLM API service.
Privacy preserving machine learning demo using TensorFlow, running as an sgx-enclave with Gramine, orchestrated by MarbleRun
Prepare a Virtual Machine libvirt XML config and the host to match a specific scenario usage
AMD SEV Python library and CLI tool for parsing and verifying attestation reports.
Artifact for the paper Architecting Trigger-Action Platforms for Security, Performance and Functionality (NDSS '24)
A repository of larger example Veracruz computations
Port of the DeathStar benchmark for microservices to a confidential computing setting.
GKE Sandbox for Confidential ML Inference