Confidential Computing: Building Secure Enclaves and Privacy-First Systems

// SGX enclave code
#include <sgx_enclave_common.h>
#include <sgx_urts.h>

// Enclave function
public void process_secure_data(
    uint8_t* input_data,
    size_t input_size,
    uint8_t* output_data,
    size_t* output_size
) {
    // This code runs in protected enclave
    // Cannot be accessed by OS, hypervisor, or other software
    
    // Process sensitive data
    encrypt_data(input_data, input_size, output_data, output_size);
}

// ECALL - Call from untrusted to trusted
sgx_status_t ecall_process_data(
    sgx_enclave_id_t eid,
    uint8_t* input_data,
    size_t input_size,
    uint8_t* output_data,
    size_t* output_size
) {
    // Create enclave instance
    sgx_launch_token_t token = {0};
    int updated = 0;
    
    sgx_create_enclave(
        ENCLAVE_PATH,
        SGX_DEBUG_FLAG,
        &token,
        &updated,
        &eid,
        NULL
    );
    
    // Process in enclave
    process_secure_data(input_data, input_size, output_data, output_size);
    
    return SGX_SUCCESS;
}

# Remote attestation verification
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import padding

class AttestationService:
    """Verify enclave attestation."""
    
    def __init__(self, enclave_public_key):
        self.enclave_key = enclave_public_key
    
    def verify_attestation(self, quote: bytes, expected_data: dict) -> bool:
        """Verify SGX quote from enclave."""
        
        # Verify quote structure
        if not self.verify_quote_structure(quote):
            return False
        
        # Verify MRENCLAVE (measurement)
        mr_enclave = self.extract_mrenclave(quote)
        if mr_enclave != expected_data["expected_enclave"]:
            return False
        
        # Verify REPORTDATA
        report_data = self.extract_report_data(quote)
        expected = self.create_expected_report(expected_data)
        if report_data != expected:
            return False
        
        # Verify signature with Intel PKI
        return self.verify_intel_signature(quote)

# AMD SEV-SNP configuration
sev_configuration:
  # Secure Nested Paging
  snp_enabled: true
  
  # Memory Encryption
  encryption:
    key_bits: 256
    key_lifetime: "infinite"
  
  # Confidential VM
  cvm:
    cpu_type: "AMD EPYC"
    memory_encryption: true
    secure_nested_paging: true
    
    # Launch configuration
    launch:
      # Measure kernel/initrd
      measure_kernel: true
      measure_initrd: true
      
      # Policy
      policy:
        # Allow debugging with authorized key
        debug_with_key: false
        
        # Secrets injection
        secrets_injection: false
        
        # Migration
        migration_agent: false

// Secure world (TEE) code
#include <tee_internal_api.h>

TEE_Result process_secure_payment(
    TEE_Param params[4]
) {
    // Input parameters
    void* encrypted_card = params[0].memref.buffer;
    size_t card_size = params[0].memref.size;
    
    // Decrypt in secure world
    uint8_t decrypted[256];
    TEE_CipherInit(
        TEE_ALG_AES_GCM,
        TEE_MODE_DECRYPT,
        &key_handle,
        NULL,  // IV
        0       // IV size
    );
    
    TEE_CipherDoFinal(
        &key_handle,
        encrypted_card,
        card_size,
        decrypted,
        &out_size
    );
    
    // Process payment securely
    TEE_Result result = process_transaction(decrypted);
    
    // Return result
    params[1].memref.buffer = &result;
    params[1].memref.size = sizeof(result);
    
    return TEE_SUCCESS;
}

// Normal world (REE) call
TEE_Result process_payment(uint8_t* card_data, size_t size) {
    TEEC_Result result;
    TEEC_Session session;
    
    // Open session to Trusted Application
    TEEC_InvokeCommand(
        &session,
        CMD_PROCESS_PAYMENT,
        &operation,
        NULL
    );
    
    return result;
}

# Confidential pod configuration
apiVersion: v1
kind: Pod
metadata:
  name: confidential-app
spec:
  securityContext:
    # Enable confidential computing
    confidentialComputing:
      mode: SEV-ES
    
  containers:
    - name: app
      image: confidential-app:latest
      
      securityContext:
        # Run inside enclave
        seccompProfile:
          type: RuntimeDefault
        
      resources:
        limits:
          # Confidential memory
          confidential-memory: "2Gi"
    
  # Attestation
  attestation:
    enabled: true
    service: "attestation-service:8080"

# Kata Containers with SEV
# /etc/kata-containers/config.toml
[hypervisor.sev]
path = "/usr/bin/qemu-system-x86_64"
kernel = "/usr/share/kata-containers/vmlinuz.container"

[hypervisor.sev]
# SEV configuration
sev_enabled = true
sev_es_enabled = true
kernel_hash = "sha256:..."

# Launch template
[hypervisor.sev.launch_template]
# Encrypted memory
memory_encryption = true

# Microsoft SEAL - Homomorphic encryption
import seal

class HomomorphicEncryptor:
    """Work with data without decrypting."""
    
    def __init__(self):
        # Create encryption parameters
        parms = seal.EncryptionParameters(scheme.seal_scheme.BFV)
        
        poly_modulus_degree = 4096
        parms.set_poly_modulus_degree(poly_modulus_degree)
        
        # Set coefficient modulus
        parms.set_coeff_modulus(
            seal.coeff_modulus_128(poly_modulus_degree)
        )
        
        # Create context
        self.context = seal.SEALContext.Create(parms)
        
        # Generate keys
        self.keygen = seal.KeyGenerator(self.context)
        self.public_key = self.keygen.public_key()
        self.secret_key = self.keygen.secret_key()
        
        # Create encryptor/evaluator
        self.encryptor = seal.Encryptor(self.context, self.public_key)
        self.evaluator = seal.Evaluator(self.context)
        self.encoder = seal.IntegerEncoder(self.context)
    
    def encrypt(self, value: int):
        """Encrypt value without knowing it."""
        plain = self.encoder.encode(value)
        return self.encryptor.encrypt(plain)
    
    def compute_add(self, encrypted_a, encrypted_b):
        """Add two encrypted numbers."""
        return self.evaluator.add(encrypted_a, encrypted_b)
    
    def compute_multiply(self, encrypted_a, encrypted_b):
        """Multiply two encrypted numbers."""
        return self.evaluator.multiply(encrypted_a, encrypted_b)
    
    def decrypt(self, encrypted_result):
        """Decrypt final result."""
        plain = self.encryptor.decrypt(encrypted_result)
        return self.encoder.decode_int32(plain)

# Privacy-preserving analytics
class PrivacyPreservingAnalytics:
    """Analytics on encrypted data."""
    
    def __init__(self):
        self.encryptor = HomomorphicEncryptor()
    
    def sum_encrypted_values(self, encrypted_values: list):
        """Sum encrypted values without decryption."""
        result = encrypted_values[0]
        
        for value in encrypted_values[1:]:
            result = self.encryptor.compute_add(result, value)
        
        return self.encryptor.decrypt(result)
    
    def average_encrypted_values(self, encrypted_values: list):
        """Calculate average of encrypted values."""
        total = self.sum_encrypted_values(encrypted_values)
        
        # Divide by count (still encrypted)
        divisor = self.encryptor.encrypt(len(encrypted_values))
        average = self.encryptor.compute_multiply(total, divisor)
        
        return self.encryptor.decrypt(average)

# Medical research on encrypted patient data
class MedicalResearchPlatform:
    """Process encrypted medical records."""
    
    def train_model(self, encrypted_records):
        """Train ML model without exposing patient data."""
        # Model training happens on encrypted data
        # Only encrypted weights are returned
        return self.model.fit_encrypted(encrypted_records)
    
    def predict(self, encrypted_patient_data, model):
        """Make predictions on encrypted data."""
        return self.model.predict_encrypted(encrypted_patient_data)

# Fraud detection on encrypted transactions
class SecureFraudDetection:
    """Detect fraud without seeing transaction details."""
    
    def detect_fraud(self, encrypted_transactions):
        """Analyze transactions in encrypted domain."""
        # Calculate features on encrypted data
        features = self.extract_features(encrypted_transactions)
        
        # Run fraud detection
        return self.fraud_model.predict(features)

# Keep trusted computing base small
tcb_minimization = {
    "principle": "Only essential code in enclave",
    "practices": [
        "Minimal API surface",
        "No external dependencies in enclave",
        "Verified boot chain",
        "Remote attestation required"
    ]
}

# Secure key handling
secure_key_management = {
    "derivation": "Derive keys from attestation",
    "rotation": "Automated key rotation",
    "storage": "Hardware security module",
    "backup": "Encrypted backups with separate keys"
}

# Comprehensive attestation
attestation_checklist = [
    "Verify enclave measurement (MRENCLAVE)",
    "Verify code signing identity",
    "Verify platform state (TEE initialized correctly)",
    "Verify secure boot state",
    "Verify OS/hypervisor configuration"
]