Private LLM Inference

Trusted Execution Environments (TEEs) enable secure and private large language model (LLM) inference, ensuring that sensitive computations occur within an encrypted and isolated environment.

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Core functionality

The core functionality focuses on securely performing privacy-preserving computations, including private LLM inference, while ensuring data confidentiality and integrity at every stage of execution.

• Secure model loading: Loads encrypted models into the TEE, preventing unauthorized access.
• Protected inference: Runs entire inference workflows securely within the enclave.
• Secure output handling: Ensures results remain encrypted and protected during transmission and storage.

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Key components

The key components enable the secure management and execution of model inference workflows, ensuring efficiency, accuracy, and robust protection throughout the process.

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Model management

• Encrypted model storage: Stores models in encrypted format, preventing unauthorized access.
• Secure weight loading: Loads model weights securely into the enclave to ensure integrity and authenticity.
• Version control & updates: Implements secure version tracking and controlled updates to prevent tampering.

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Inference pipeline

• Input preprocessing in TEE: Cleans, formats, and normalizes incoming data before inference.
• Batched inference processing: Groups multiple requests to optimize efficiency and resource usage.
• Output post-processing: Ensures results remain encrypted, preventing data leaks or unauthorized exposure.

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Security measures

• Model encryption at rest: Encrypts model weights before and after inference to prevent unauthorized access.
• Secure inference runtime: Ensures that inference operations only execute within verified TEEs.
• Memory protection: Prevents unauthorized memory access, mitigating side-channel attacks.

The following Go structs define the TEE-based LLM inference architecture, ensuring secure model execution and data protection.

type LLMInference struct {
    ModelID     string
    ModelConfig ModelSettings
    Runtime     TEERuntime
}

type ModelSettings struct {
    // Model configuration
    BatchSize      int
    Precision      string
    MaxInputLength int
    // Security settings
    EncryptionKey  []byte
    AccessControl  Policy
}

type InferenceRequest struct {
    // Input data for inference
    Input []byte
    // Metadata for processing
    Metadata RequestMetadata
    // Security context
    SecurityContext Context
}