Confidential Computing vs Homomorphic Encryption: When to Use Each

Meta Description: Compare confidential computing and homomorphic encryption for privacy-preserving computation. Learn which technology fits your use case, performance needs, and security requirements.

Target Keywords: confidential computing vs homomorphic encryption, FHE vs TEE, privacy preserving computation comparison, encrypted computation methods

Reading Time: 14 minutes

TL;DR - Confidential Computing vs Homomorphic Encryption

Quick Comparison:

Recommendation:

• Most use cases: Confidential Computing (practical today)
• Specific scenarios: Homomorphic Encryption (when you cannot trust any hardware)
• Best approach: Combine both (TEE for performance + HE for specific operations)

Understanding the Technologies

Confidential Computing (TEE-Based)

How It Works:

Key Principle: Data is decrypted inside a hardware-protected environment (TEE) for processing, then re-encrypted before leaving.

Trust Assumption: You trust the CPU/GPU hardware (AMD, Intel, NVIDIA) not to have backdoors.

Confidential Computing Workflow:

• Client connects to confidential AI service (e.g., Phala Cloud API)
• Client sends encrypted query to GPU TEE endpoint
• Query decrypted ONLY inside GPU TEE hardware
• AI model processes plaintext request in secure memory
• Result encrypted before leaving TEE environment
• Client receives and decrypts response

Key Benefit: Near-native performance for complex AI workloads (LLMs, vision models)

Homomorphic Encryption (FHE)

How It Works:

Key Principle: Data NEVER gets decrypted during computation. Math happens directly on ciphertext.

Trust Assumption: You don’t trust anyone—not the cloud, not the hardware. Only trust mathematics.

Homomorphic Encryption Workflow:

• Client encrypts data locally using FHE scheme
• Send encrypted values to untrusted server
• Server performs computation on encrypted data
• Server returns encrypted result
• Client decrypts result locally

Key Benefit: Zero-trust model—server never accesses plaintext data at any point

Deep Dive: Confidential Computing

How TEEs Protect Data

Architecture:

Performance:

• Overhead: 2-15% (near-native speed)
• Why Fast: Hardware-accelerated encryption (dedicated circuits in CPU/GPU)
• Scalability: Handles large datasets, complex computations (AI training, databases)

Strengths

• Performance: Run any computation at near-native speed
• Flexibility: Support any programming language, algorithm, framework
• Ease of Use: Deploy existing code without modification
• Maturity: Production-ready (millions of VMs in production)
• Ecosystem: Rich tooling (Docker, Kubernetes, cloud platforms)

Limitations

• Hardware Trust: Must trust CPU/GPU vendor (Intel, AMD, NVIDIA)
• Supply Chain Risk: Theoretical risk of hardware backdoors
• Side Channels: Potential for timing attacks, cache attacks (mitigated but not eliminated)
• Cloud Provider Access: Cloud provider can disrupt service (DoS), though cannot access data

Best Use Cases

• AI/ML: Training and inference on sensitive data
• Databases: Confidential queries on regulated data
• Web Applications: End-to-end confidential processing
• Multi-Party Computation: Multiple parties processing shared data
• Collaborative Analytics: Joint analysis without data sharing

Real Example: Hospital uses Phala Cloud GPU TEE to train cancer detection AI on patient records. TEE protects data during training; cloud provider cannot access patient data.

Deep Dive: Homomorphic Encryption

Types of Homomorphic Encryption

1. Partially Homomorphic Encryption (PHE)

• Supports ONE operation: either addition OR multiplication
• Examples: RSA (multiplication), Paillier (addition)
• Use Case: Simple encrypted voting, basic statistics

2. Somewhat Homomorphic Encryption (SHE)

• Limited number of both additions and multiplications
• Performance: Moderate (10-100x slower)
• Use Case: Simple encrypted queries

3. Fully Homomorphic Encryption (FHE)

• Unlimited additions and multiplications (arbitrary computation)
• Performance: Very slow (100-10,000x slower)
• Use Case: Complex encrypted computation when no hardware can be trusted

How FHE Works (Simplified)

Example: Encrypted Addition

Plaintext: 5 + 3 = 8

With FHE:
1. Encrypt: E(5) and E(3)
2. Compute on ciphertext: E(5) ⊕ E(3) = E(8)
3. Decrypt: D(E(8)) = 8

Why It’s Slow:

• Ciphertext is MUCH larger than plaintext (100x to 1000x)
• Each operation requires complex lattice math
• “Noise” accumulates, requiring periodic refreshing (bootstrapping)

Performance Reality

Benchmarks (Concrete-ML FHE Library):

Key Insight: Simple operations (addition, comparison) are feasible. Complex operations (ML inference, AES) are still impractical.

Current Maturity

Production-Ready:

• ✅ Simple voting systems
• ✅ Encrypted queries on databases (simple aggregations)
• ✅ Private set intersection

Research/Emerging:

• 🔬 Encrypted machine learning inference (getting faster)
• 🔬 Encrypted database operations (limited functionality)
• 🔬 General-purpose FHE computation (still 100-1000x too slow)

Strengths

• Zero Trust: No hardware, cloud, or admin trust required
• Mathematical Security: Based on hard math problems (lattices)
• Auditability: Cryptography is open-source and peer-reviewed
• Future-Proof: Quantum-resistant (most schemes)

Limitations

• Performance: 100-10,000x slower than native (major blocker)
• Complexity: Requires algorithm redesign (cannot use standard code)
• Limited Operations: Many algorithms don’t translate to FHE efficiently
• Large Ciphertext: 100-1000x storage overhead
• Immature Ecosystem: Few libraries, limited production deployments

Best Use Cases

• Encrypted Voting: Simple tallying without revealing individual votes
• Private Information Retrieval: Query database without revealing query
• Simple Analytics: Sum, average, count on encrypted data
• Regulatory Compliance: When no hardware can be trusted (extreme scenarios)

Real Example: Government election system uses FHE to tally votes without ever decrypting individual ballots (no risk of vote manipulation).

Head-to-Head Comparison

Performance Comparison

Conclusion: TEE is faster for ALL general-purpose workloads. HE only wins when you cannot decrypt at all.

Security Comparison

Conclusion: HE provides stronger theoretical security (no hardware trust). TEE provides strong practical security (trusted hardware).

Ease of Use Comparison

Conclusion: TEE is FAR easier to implement. HE requires specialized expertise.

Cost Comparison

Example: Running an AI model:

• TEE: $5/hour GPU TEE (1.1x cost of standard GPU)
• FHE: $500-5,000/hour (100-1000 CPUs to match performance) — IMPRACTICAL

Conclusion: TEE is economically viable. FHE is cost-prohibitive for most uses.

When to Use Each Technology

Use Confidential Computing (TEE) When:

✅ Performance matters (most applications)

✅ Complex computations (AI, databases, general apps)

✅ Existing codebases (want to reuse without rewriting)

✅ Time-to-market is critical (days/weeks not months/years)

✅ You can trust major CPU vendors (Intel, AMD, NVIDIA)

✅ Budget is constrained (TEE is 10-30% premium, FHE is 100-10,000x)

Examples:

• Confidential AI training on healthcare data (Phala Cloud GPU TEE)
• Encrypted database queries (PostgreSQL in Confidential VM)
• Multi-party data analytics (Secure enclaves on Google Cloud)

Use Homomorphic Encryption (HE) When:

✅ Zero hardware trust (extreme security scenario)

✅ Simple operations (voting, basic stats, simple queries)

✅ Regulatory mandate (specific requirement for math-based encryption)

✅ Long-term data confidentiality (quantum-resistant needed)

✅ Public verifiability (anyone can verify computation without decryption)

Examples:

• Government election vote tallying
• Simple encrypted database queries (count, sum, average on small datasets)
• Private set intersection (find common elements without revealing sets)

Use BOTH (Hybrid Approach)

✅ Combine strengths: TEE for heavy computation + HE for specific operations

✅ Defense in depth: Multiple layers of protection

Example Architecture:

Benefit: HE prevents data exposure, TEE prevents side-channel leaks from FHE implementation.

Real-World Examples

Example 1: Healthcare AI (TEE Wins)

Scenario: Train cancer detection model on patient MRI scans

TEE Approach (Phala Cloud):

• Upload encrypted scans to GPU TEE
• Train ResNet-50 model: 2 hours
• Cost: $10 (2 hours × $5/hour GPU TEE)
• Result: ✅ Practical

FHE Approach:

• Encrypt scans with FHE
• Train on encrypted data: 2,000 hours (83 days!)
• Cost: $200,000 (estimate)
• Result: ❌ Impractical

Winner: Confidential Computing (TEE)

Example 2: Government Election (HE Wins)

Scenario: Tally election votes without revealing individual ballots

TEE Approach:

• Cannot work! TEE must decrypt votes to count them
• Risk: Malicious insider could export decrypted votes
• Result: ❌ Doesn’t meet requirement

FHE Approach:

• Votes encrypted with FHE
• Tally computed on ciphertext (E(vote1) + E(vote2) + …)
• Only final sum is decrypted
• Result: ✅ Perfect fit (individual votes never exposed)

Winner: Homomorphic Encryption (FHE)

Example 3: Confidential Database Queries (Hybrid)

Scenario: Hospital allows researchers to query patient database without exposing individual records

Pure TEE:

• Researcher sends query to TEE
• TEE decrypts DB, runs query, encrypts result
• Risk: Side-channel leaks from query patterns
• Result: ⚠️ Good but not perfect

Pure FHE:

• Queries on fully encrypted database
• Problem: Too slow for complex queries (hours instead of seconds)
• Result: ⚠️ Secure but impractical

Hybrid (TEE + FHE):

• Simple aggregations use FHE (count, sum)
• Complex queries use TEE with differential privacy
• Best of both: performance + strong privacy
• Result: ✅ Optimal

Winner: Hybrid Approach

The Future: Convergence

FHE Performance Improvements

Current Trajectory:

• 2020: 100,000x slower than native
• 2023: 10,000x slower (10x improvement)
• 2025: ~1,000x slower (estimated)
• 2030: ~100x slower (projected)

Key Developments:

• Hardware acceleration for FHE (FPGA, ASIC)
• Better algorithms (TFHE, CKKS optimizations)
• Specialized libraries (Concrete, SEAL, HElib)

TEE + FHE Integration

Emerging Pattern:

Why: TEE provides speed, FHE provides mathematical guarantee. Together = best security + best performance.

Practical Guidance for 2025

For Production Today:

• Use Confidential Computing (TEE) for 95% of use cases
• Use Homomorphic Encryption only for very specific scenarios (voting, simple queries)

For Future (2026-2030):

• TEE remains fastest for general computation
• FHE becomes viable for moderate-complexity computations (ML inference)
• Hybrid TEE+FHE becomes standard for maximum security

Frequently Asked Questions

Is homomorphic encryption “better” than confidential computing?

No. Different technologies for different use cases. FHE provides stronger theoretical security (no hardware trust) but is 100-10,000x slower. TEE provides strong practical security with near-native performance. Most real-world applications need TEE’s speed.

Can I use both together?

Yes! Run FHE computation inside a TEE for defense-in-depth. This protects against both hardware backdoors (FHE layer) and side-channel attacks on FHE implementation (TEE layer).

Will FHE replace confidential computing?

Unlikely. Even with decades of improvement, FHE will likely remain 10-100x slower than native. TEE overhead is already <10% and improving. FHE is a specialized tool, not a general replacement.

Which is more secure: TEE or FHE?

FHE is theoretically more secure (no hardware trust, quantum-resistant). TEE provides strong practical security today. For 99% of threats (malicious cloud admins, hackers), both are equally effective. FHE only wins in extreme scenarios (nation-state hardware backdoors, quantum computers).

What about differential privacy?

Differential privacy is orthogonal—it protects against inference attacks by adding noise to results. Use it WITH TEE or FHE:

• TEE + Differential Privacy = Most practical approach today
• FHE + Differential Privacy = Maximum theoretical security

Can I just use FHE instead of learning TEE?

Not recommended. FHE requires cryptography expertise and months/years of development. TEE works with standard code and can be deployed in days/weeks. Start with TEE unless you have a specific reason to use FHE.

Conclusion

For most use cases: Confidential Computing (TEE) is the right choice in 2025.

Use TEE for:

• AI/ML workloads (training, inference)
• Databases and analytics
• Web applications
• Any performance-sensitive application

Use FHE for:

• Encrypted voting systems
• Simple queries on encrypted data
• Scenarios where NO hardware can be trusted

Best approach: Start with TEE, add FHE for specific operations if needed.

Phala Cloud provides production-ready TEE infrastructure for confidential AI and general workloads, making it easy to deploy secure applications without the complexity of FHE.

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

Published: November 3, 2025

Category: Implementation Guides

Keywords: confidential computing vs homomorphic encryption, TEE vs FHE, privacy preserving computation, encrypted computation comparison, HE performance