What is confidential computing in the context of SecAI+ and where is it useful?

• A. Running computations in trusted execution environments or enclaves to protect data and models during processing.
• B. Running computations on public clouds without encryption.
• C. Performing data replication for redundancy.
• D. Reducing model size to fit devices.

Answer: (A)

Confidential computing means protecting data while it’s being processed. This is done by running computations inside a trusted execution environment or enclave, where the data and the AI model are kept encrypted and isolated from the rest of the system, including the host OS and even the cloud provider.

This approach is especially useful when you need to handle sensitive information in potentially untrusted environments, such as public clouds, multi-tenant data centers, or edge devices. It allows you to train or run inferences on private data without exposing inputs, outputs, or model details to the infrastructure provider or other tenants. It also helps protect proprietary models and intellectual property and supports regulatory requirements for privacy during computation.

In practice, trusted execution environments provide attestation to verify that code is running in a genuine enclave and enforce confidentiality and integrity protections against a compromised operating system or hypervisor. Be aware that using confidential computing may involve hardware support and some performance trade-offs, and it doesn't eliminate all risks (for example, certain side-channel concerns).