Confidential Computing: Secure Enclaves for Data Protection 2026

#include <sgx_urts.h>
#include " enclave_u.h"

#define ENCLAVE_FILE "my_enclave.signed.so"

int main() {
    sgx_enclave_id_t eid;
    sgx_status_t ret;
    sgx_launch_token_t token = {0};
    int updated = 0;
    
    // Create the enclave
    ret = sgx_create_enclave(ENCLAVE_FILE, SGX_DEBUG_FLAG,
                             &token, &updated, &eid, NULL);
    if (ret != SGX_SUCCESS) {
        printf("Failed to create enclave: %d\n", ret);
        return -1;
    }
    
    // Call an enclave function
    int secret_value = 42;
    int result;
    ret = enclave_compute(&eid, &result, secret_value);
    
    // Result is protected - even root cannot read it
    printf("Result: %d\n", result);
    
    // Destroy the enclave
    sgx_destroy_enclave(eid);
    
    return 0;
}

# Create a DCasv5-series VM (AMD SEV-SNP)
az vm create \
  --resource-group my-rg \
  --name confidential-vm \
  --size Standard_DC4as_v5 \
  --image Ubuntu22_04-gen2 \
  --enable-confidential-computing \
  --admin-username azureuser \
  --ssh-key-value ~/.ssh/id_rsa.pub

# Verify SEV-SNP is active
az vm get-instance-view \
  --resource-group my-rg \
  --name confidential-vm \
  --query "confidentialComputing"

import boto3
import json

def create_enclave(image_path):
    """Create a Nitro enclave from a Docker image."""
    nitro_client = boto3.client('nitro-enclaves')
    
    # Create enclave from Docker image
    response = nitro_client.create_enclave(
        ImagePath=image_path,
        vCPUs=2,
        MemoryMiB=2048,
        AllowedProtocols=['vsock'],
        AllowedServices=['kms']
    )
    
    return response['EnclaveId']

def communicate_with_enclave(enclave_id):
    """Send data to enclave via vsock."""
    import socket
    
    # Nitro enclave listens on port 9000
    sock = socket.socket(socket.AF_VSOCK, socket.SOCK_STREAM)
    sock.connect((enclave_id, 9000))
    
    # Send sensitive data
    sensitive_data = {"ssn": "123-45-6789"}
    sock.send(json.dumps(sensitive_data).encode())
    
    result = sock.recv(4096)
    return json.loads(result)

# Create a confidential VM
gcloud compute instances create confidential-vm \
    --machine-type n2d-standard-4 \
    -- Confidential VMs are automatically enabled
    --image-family confidential-vm-family \
    --image-project gce-uefi-images \
    --boot-disk-size 50GB \
    --boot-disk-type pd-ssd

# Process medical records in an enclave
class MedicalRecordProcessor:
    def __init__(self, enclave):
        self.enclave = enclave
    
    def analyze_patient_data(self, encrypted_records):
        """
        Analyze patient data without exposing it to cloud provider.
        """
        # Send encrypted data to enclave
        result = self.enclave.execute(
            'analyze_records',
            encrypted_data=encrypted_records
        )
        
        # Receive only aggregate results
        return result  # e.g., "95% of patients show improvement"
    
    def train_medical_ai(self, dataset):
        """
        Train AI model on sensitive medical data.
        Model is protected, data never leaves enclave.
        """
        return self.enclave.execute('train_model', dataset=dataset)

// Private transaction contract using TEE
contract PrivateTransaction {
    EnclaveAddress public trustedEnclave;
    
    function processTransaction(
        bytes32 encryptedData,
        bytes32 enclaveSignature
    ) public {
        // Verify enclave attested by trusted party
        require(verifyAttestation(enclaveSignature));
        
        // Decrypt and process in TEE
        (address from, address to, uint amount) = 
            trustedEnclave.decrypt(encryptedData);
        
        balances[from] -= amount;
        balances[to] += amount;
        
        emit TransactionProcessed(from, to, amount);
    }
}

import hashlib
import time

class AttestationService:
    def __init__(self, enclave):
        self.enclave = enclave
        self.trusted_hashes = self.load_trusted_hashes()
    
    def verify_attestation(self, attestation_quote):
        """
        Verify the enclave is genuine and hasn't been tampered.
        """
        # 1. Verify MRENCLAVE (measurement)
        mr_enclave = extract_mrenclave(attestation_quote)
        if mr_enclave not in self.trusted_hashes:
            raise SecurityError("Untrusted enclave measurement")
        
        # 2. Verify user data hash
        expected_hash = self.enclave.get_user_data_hash()
        user_data_hash = extract_user_data_hash(attestation_quote)
        if expected_hash != user_data_hash:
            raise SecurityError("User data mismatch")
        
        # 3. Verify timestamp (freshness)
        timestamp = extract_timestamp(attestation_quote)
        if time.time() - timestamp > 300:  # 5 minutes
            raise SecurityError("Stale attestation")
        
        return True
    
    def load_trusted_hashes(self):
        """Load known-good enclave measurements."""
        return {
            "a1b2c3d4...": "production-enclave-v2.1",
            "e5f6g7h8...": "ml-inference-enclave-v1.5",
        }

class EnclaveKeyManager:
    """Manage keys securely within enclaves."""
    
    def __init__(self, kms_client, key_id):
        self.kms_client = kms_client
        self.key_id = key_id
    
    def get_enclave_key(self, enclave_id):
        """
        Derive a key specific to an enclave instance.
        """
        # Key derived from: master key + enclave identity
        master_key = self.kms_client.get_key(self.key_id)
        
        enclave_key = hashlib.pbkdf2_hmac(
            'sha256',
            master_key,
            enclave_id.encode(),
            iterations=100000,
            dklen=32
        )
        
        return enclave_key
    
    def wrap_data_key(self, data_key, enclave_id):
        """
        Wrap (encrypt) a data key for a specific enclave.
        """
        enclave_key = self.get_enclave_key(enclave_id)
        
        return encrypt_aes_gcm(data_key, enclave_key)

# confidential-container.yaml
apiVersion: v1
kind: Pod
metadata:
  name: confidential-pod
spec:
  runtimeClassName: confidential-cc
  containers:
  - name: app
    image: myapp:confidential
    securityContext:
      confidential: true
      tdx: true  # Intel TDX support

# Confidential AI inference
class ConfidentialInference:
    def __init__(self, model_enclave):
        self.enclave = model_enclave
    
    def predict(self, sensitive_input):
        """
        Run inference without exposing:
        - The input data
        - The model weights
        - The output predictions
        """
        return self.enclave.run_inference(sensitive_input)