FHE for Biometric Authentication:
Privacy-Preserving Identity Verification

Biometric authentication—using face, fingerprint, or voice to verify identity—is increasingly common. But biometric data is uniquely sensitive: you can't change your fingerprints if they're compromised. Fully Homomorphic Encryption (FHE) solves this by enabling biometric matching on encrypted data.

The Biometric Privacy Problem

Traditional biometric systems have serious privacy concerns:

• Template storage: Raw templates stored on servers can be stolen
• Matching exposure: Templates must be decrypted for comparison
• Irreversibility: Unlike passwords, compromised biometrics can't be changed
• Linkability: Same biometrics across services enable tracking

High-profile breaches (OPM, Biostar 2) have exposed millions of biometric records, underscoring these risks.

FHE Biometric Architecture

FHE fundamentally changes biometric authentication:

How It Works Technically

Biometric matching typically involves computing similarity between templates (often Euclidean distance or cosine similarity). With FHE:

// Conceptual FHE biometric matching
// Templates are vectors of encrypted values

// Encrypted enrollment template (stored)
enrolledTemplate = [E(e1), E(e2), ..., E(en)]

// Encrypted verification template (new)
verifyTemplate = [E(v1), E(v2), ..., E(vn)]

// Compute encrypted squared differences
for i in range(n):
  diff[i] = E(e_i) - E(v_i)
  squared[i] = diff[i] * diff[i]

// Sum for Euclidean distance squared
encryptedDistance = sum(squared)

// Decrypt only the distance, compare to threshold
distance = decrypt(encryptedDistance)
match = distance < threshold

Performance at H33

We've optimized FHE biometric matching for real-time use:

• FHE encryption: 150µs for template encryption
• Encrypted matching: 260µs for distance computation
• Full verification: 1.28ms end-to-end (Turbo mode)
• Accuracy: 99.7% match rate, comparable to plaintext systems

This performance makes FHE biometrics practical for real-time authentication.

Security Benefits

FHE biometrics provide multiple security layers:

• Server breach protection: Stolen encrypted templates are useless without keys
• Insider threat mitigation: Administrators can't access raw biometrics
• Regulatory compliance: Data minimization—servers only process what they need
• User control: Users hold decryption keys for their biometrics

Biometric Types Supported

FHE works with various biometric modalities:

• Face recognition: FHE-encrypted facial embeddings
• Fingerprint: Encrypted minutiae matching
• Voice: Encrypted voice print comparison
• Iris: Encrypted iris code matching

Each modality has different template sizes and matching algorithms, but FHE accommodates all.

Implementation Considerations

When implementing FHE biometrics:

• Template format: Standardize on numerical vectors (embeddings work well)
• Key management: Users should control their FHE keys
• Fallback: Plan for key loss scenarios
• Performance tuning: Optimize FHE parameters for your specific templates

Combining with Other Technologies

FHE biometrics work well with complementary technologies:

• Zero-knowledge proofs: Prove match without revealing distance
• Blockchain: Immutable audit trail without exposing data
• Secure enclaves: Additional protection for key operations

H33's Full Stack Auth combines all these for comprehensive protection.

Real-World Applications

FHE biometrics are being adopted across sectors:

• Mobile banking with privacy-preserving face verification
• Healthcare patient identification without central biometric databases
• Border control systems that verify without storing traveler biometrics
• Enterprise access control with privacy-first authentication

FHE biometric authentication represents the future of identity verification—providing strong security while respecting user privacy. The technology is production-ready today.