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.
Documentation related to Intel's Confidential Computing offerings
🚧 SDK for building verifiable AI Agents on Flare using Confidential Space
The Cape Privacy Python SDK
Open-source FHE client and toolchain. Build fully homomorphic encryption applications with the nb DSL, instrumented OpenFHE, FHETCH API or a CUDA-style library API, record one Polynomial IR trace, and deploy to the Niobium accelerator
Sample functions for Cape Privacy
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.
Accountable Autonomy: open-source evidence layer that gates every AI agent tool call against your policy and writes a hash-chained record an auditor verifies offline, without trusting you. Root-agnostic; binds to TPM 2.0 / SEV-SNP when present. Your environment, no SaaS, no telemetry. AGPL-3.0.
A TEE deployment auditor for the trust boundaries attestation forgets — audits Nitro Enclave bootstrap state, KMS attestation, IAM, and host posture (33 checks).
a sdk tool to access capsule manager
A tool for developers and researchers to design FHE pipelines and execute them on cloud-based GPUs
Privacy preserving machine learning demo using TensorFlow, running as an sgx-enclave with Gramine, orchestrated by MarbleRun
Example code that leverages Attested-TLS protocol (GENXT confido lib) to verify and access Confidential LLM API service.
🛡️ Local privacy firewall for AI coding agents — masks API keys & personal data before they reach the cloud, then restores them transparently.
One toolkit, every agent — drop decentralized, all-TEE, OpenAI-compatible Chutes.ai inference into Claude, Hermes, OpenClaw, Codex, or any OpenAI-compatible client.