TEE as a general hardware-based confidential computation infrastructure can offer a practical solution compared to other cryptographic methods like ZK and FHE in AI Inference:
- The computational overhead is significantly lower, with nearly native speed of execution
- Verification using TEEs is also more economical compared to ZKPs. An ECDSA signature suffices for on-chain verification, reducing the complexity and cost of ensuring computation integrity.
- Native Support by NVIDIA in the series of GPUs such as H100 and H200 natively support TEEs, providing hardware-accelerated secure environments for AI workloads. This native support ensures seamless integration and optimized performance for confidential AI inference.
Our TEE-based solution can provide following features for AI Inference:
- Tamper-Proof Data: Ensuring that user request/response data cannot be altered by a middleman is fundamental. This necessitates secure communication channels and robust encryption mechanisms.
- Secure Execution Environment: Both hardware and software must be protected against attacks. This involves leveraging TEE that provides isolated environments for secure computation.
- Open Source and Reproducible Builds: The entire software stack, from the operating system to the application code must be reproducible. This allows auditors to verify the integrity of the system.
- Verifiable Execution Results: The results of AI computations must be verifiable, ensuring that the outputs are trustworthy and have not been tampered with.