Trusted Execution Environments (TEE)

What is a TEE? A secure area of a processor that runs code and stores data protected from the main operating system, providing hardware-based security guarantees.

How TEEs Work

[Normal World]          [Secure Enclave]
    |                        |
Application --(encrypted)--> Protected Code
    |                        |
    OS                 [Isolated Memory]
    |                        |
Hypervisor            [Hardware Protection]

TEE Technologies

Intel SGX Concepts

Confidential Computing Use Cases

# Conceptual: Run ML inference in enclave
def secure_inference():
    # Inside enclave
    model = load_encrypted_model()
    model.decrypt_with_enclave_key()

    # Process encrypted input
    encrypted_input = receive_from_client()
    decrypted_input = decrypt_in_enclave(encrypted_input)

    # Run inference
    result = model.predict(decrypted_input)

    # Re-encrypt result
    return encrypt_for_client(result)

Benefits

Limitations

Cloud Confidential Computing

ML in TEEs

• Run inference on private data
• Protect model weights from cloud
• Multi-party computation
• Confidential training

Best Practices

• Use attestation to verify enclave
• Minimize enclave surface area
• Handle side-channel risks
• Test thoroughly before deployment