# Trusted Execution Environments (TEE)

**Trusted Execution Environments (TEEs)** are a critical component of StarMiner’s security and privacy stack. They enable compute jobs especially those involving sensitive datasets or proprietary AI models to be executed in a fully isolated, hardware-enforced enclave, where the **data, process, and results remain confidential**, even from the node executing them.

By integrating TEEs across its global compute layer, StarMiner supports **enterprise-grade trust**, privacy compliance (e.g., GDPR, HIPAA), and confidential AI workloads without centralizing control or requiring third-party verification.

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**What Is a TEE?**

A TEE is a secure area within a processor that runs code in isolation from the rest of the system — including the operating system, hypervisor, and node operator. Examples include:

* **Intel SGX**
* **AMD SEV**
* **ARM TrustZone**

Inside a TEE:

* Code and data are encrypted in memory
* External access to running jobs is blocked
* Integrity checks prevent tampering or debugging

This ensures a level of **execution integrity and data confidentiality** that software-based sandboxing or traditional cloud environments cannot match.

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**StarMiner TEE Architecture**

StarMiner does not rely on one TEE vendor or standard. Instead, the protocol supports a **modular TEE interface**, allowing compatible Provider Nodes to offer “confidential compute capacity” via:

1. **Job-Type Tagging**
   * Requesters can flag workloads as privacy-sensitive (e.g., medical data, proprietary LLM training).
   * The protocol routes these tasks exclusively to TEE-capable nodes.
2. **TEE-Aware Routing Layer**
   * The computing protocol layer recognizes and catalogs TEE-certified nodes.
   * Only verified enclaves with active attestation keys are eligible to receive tagged tasks.
3. **Remote Attestation & Proof-of-Execution**
   * TEEs generate cryptographic proof that:
     * The correct code was executed inside the enclave
     * No unauthorized access occurred during runtime
   * These proofs are submitted on-chain or through off-chain oracles for auditing and verification.
4. **Encrypted Task Flow**
   * Inputs and model parameters are encrypted at the application layer.
   * They are decrypted only inside the TEE during execution, and re-encrypted before being returned to the requester.

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**Use Cases for TEE in StarMiner**

* **Confidential AI**: Training or inference on proprietary datasets (e.g., medical, legal, financial) without exposing content to the node operator.
* **Regulated Industries**: Industries requiring demonstrable compliance with data privacy laws.
* **Multi-Party Computation**: Enabling shared model training without exposing each party’s raw data.
* **Zero-Knowledge Infrastructure**: Supporting verifiable off-chain computation as a precursor to Zero-Knowledge Machine Learning (ZKML) integration.

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**Incentives and Economic Design**

TEE nodes may:

* Earn **higher AGPU rates** for executing sensitive workloads
* Access exclusive job tiers unavailable to non-TEE nodes
* Receive **sustainability or compliance bonuses** via DAO governance

To maintain eligibility, nodes must maintain **certified hardware**, **uptime SLAs**, and submit **ongoing attestation proofs**.

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**Security Advantages**

* Data confidentiality from untrusted node operators
* Execution integrity validated cryptographically
* Protected against tampering, side-channel attacks, and runtime observation
* Lower legal and compliance risk for enterprises using public infrastructure

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#### Summary

Trusted Execution Environments give StarMiner a competitive edge in decentralized AI compute by offering **confidentiality without compromise**. TEEs enable trustless nodes to perform trusted computation making StarMiner the only network capable of securely handling sensitive, mission-critical tasks at scale.

This unlocks a new class of clients and industries for decentralized computing from healthcare to finance where privacy and integrity are non-negotiable.
