Confidential Computing vs Multi-Party Computation (MPC): Complete Comparison

Meta Description: Compare confidential computing (TEE) and multi-party computation (MPC) for secure data collaboration. Learn when to use each privacy-preserving technology.

Target Keywords: confidential computing vs MPC, TEE vs multi-party computation, secure computation comparison, MPC vs TEE

Reading Time: 15 minutes

TL;DR - Confidential Computing vs MPC

Quick Comparison:

When to Use:

• TEE: One organization processing sensitive data, or multi-party with trusted coordinator
• MPC: Multiple parties jointly computing, no single trusted party exists
• Both: TEE nodes running MPC protocols (best security + performance)

Understanding the Technologies

Confidential Computing (TEE)

How It Works:

Trust Assumption: All parties trust the hardware vendor and TEE implementation.

Example (Phala Cloud):

from phala_sdk import upload_to_tee, tee_train_model

# Hospital A uploads encrypted patient data
upload_to_tee("hospital-a-patients.enc", tee_id="abc123")

# TEE decrypts all data, trains model, encrypts result
trained_model = tee_train_model(tee_id="abc123")

Multi-Party Computation (MPC)

How It Works:

Trust Assumption: No single party is trusted; requires majority honest (threshold security).

Deep Dive: Multi-Party Computation (MPC)

How MPC Achieves Security

Secret Sharing Example:

• Split a secret into random shares.
• Distribute shares among parties.
• Compute on shares without revealing the original secret.

MPC Types

Performance Reality

Benchmarks (Typical MPC Libraries):

MPC Strengths

• No Trusted Third Party: Ideal for competitors or mutually distrusting parties.
• Provable Security: Based on cryptographic assumptions.
• Threshold Security: Works even if a minority of parties are malicious.

MPC Limitations

• Performance: 10-1,000x slower than native.
• Network Requirements: Constant communication needed.
• Complexity: Requires cryptographic expertise.

Deep Dive: Confidential Computing for Multi-Party Scenarios

How TEE Enables Multi-Party Computation

Architecture:

Trust Model: Parties trust the TEE hardware, not each other or the cloud operator.

Performance: Near-native speed (2-15% overhead).

Attestation as Trust Mechanism

Key Advantage: TEE provides cryptographic proof of execution.

from phala_sdk import verify_attestation

attestation = get_attestation_report("tee-app-id")
if verify_attestation(attestation):
    upload_encrypted_data("bank-a-transactions.enc")
else:
    raise SecurityError("TEE verification failed!")

Head-to-Head Comparison

Performance Comparison

Security Comparison

Trust Model Comparison

Confidential Computing:

• Trust in CPU/GPU vendor.
• Verify TEE via attestation.

MPC:

• No one is trusted individually.
• Trust in cryptographic protocols.

Complexity Comparison

When to Use Each Technology

Use Confidential Computing (TEE) When:

✅ Centralized coordinator exists

✅ Performance is critical

✅ Quick deployment

Examples:

• Healthcare consortium with one hospital hosting TEE.
• Financial firms using neutral TEE provider (Phala Cloud).

Use Multi-Party Computation (MPC) When:

✅ No trusted party

✅ Simple computations

✅ Hardware cannot be trusted

Examples:

• Competitor firms computing joint statistics.
• Secure voting where no election authority can be trusted.

Use BOTH (Hybrid: TEE + MPC)

✅ Maximum security

✅ Best performance

Example Architecture:

Benefit: Even if one party’s TEE is compromised, MPC ensures data stays safe.

Real-World Case Studies

Case Study 1: Financial Fraud Detection

Scenario: 10 banks want to train a joint fraud model without sharing customer data.

Option B: TEE (Phala Cloud)

• Implementation Time: 2 weeks
• Training Time: 4 hours
• Cost: ~$20
• Security: Banks must trust NVIDIA H100

Decision: Banks chose TEE because:

1. All banks already trust NVIDIA.
2. 50x faster time-to-market.
3. 2,500x lower cost.

Case Study 2: Government Election Tallying

Scenario: Tally election votes without any single authority seeing individual ballots.

Option B: MPC

• Threshold: Any 3 of 5 nodes can tally.
• Security: Must compromise 3+ nodes to cheat.

Decision: Government chose MPC because:

1. Cannot trust any single election authority.
2. Decentralization prevents single point of failure.

Combining TEE and MPC (Best Practice)

Architecture Pattern

Benefits of Hybrid Approach

1. Defense in Depth: Must compromise both hardware AND MPC threshold.
2. Performance: TEE adds only 5-10% overhead to MPC.
3. Trust Flexibility: Parties can choose to trust hardware OR rely on MPC threshold.

The Future: Convergence

Trends

1. TEE-Accelerated MPC

• Best security (MPC) + Best performance (TEE).

2. Standardization

• Common APIs for TEE and MPC.

3. Hybrid Protocols

• Automatic fallback: TEE for performance, MPC for critical operations.

Predictions for 2025-2030

TEE:

• Becomes default (all cloud VMs confidential).
• <2% overhead.

MPC:

• 10x performance improvement.

Hybrid:

• Becomes standard for high-security scenarios.

Frequently Asked Questions

Is MPC more secure than confidential computing?

Different security models. MPC doesn’t require hardware trust. TEE requires trusting CPU/GPU vendor but is secure in practice.

Can I use MPC and TEE together?

Yes! Run MPC protocols inside TEEs for maximum security. Each party operates an MPC node in their own TEE, verified by attestation.

Which is faster: MPC or confidential computing?

TEE is 10-1,000x faster. Choose TEE unless you need MPC’s trust model.

Do I need MPC if I have confidential computing?

Usually no. Use MPC only when:

1. No party can be trusted to host TEE.
2. Decentralization is required.

Which should I learn first?

Learn confidential computing (TEE) first. It’s more practical and easier to implement.

Conclusion

For most confidential computing scenarios: Use TEE

• Near-native performance
• Easy to deploy

For specialized multi-party scenarios: Use MPC

• No trusted third party needed

For maximum security: Use Both (Hybrid)

• TEE protects from cloud/infrastructure
• MPC protects from malicious parties

Phala Cloud provides TEE infrastructure ideal for both standalone confidential computing and as nodes in hybrid TEE+MPC deployments.

Related Resources

• Phala Homepage
• Learning Hub
• Phala Cloud Documentation
• Dstack Documentation
• Success Stories

Next Steps

• Get Started with Phala Cloud
• Deploy Your First Confidential VM
• Explore Confidential AI
• Join Our Startup Program
• Contact Us