BFV lattice-based fully homomorphic encryption. N=4096 polynomial ring, 56-bit modulus, SIMD batching. 32 users verified in a single 1,109µs ciphertext operation.
Three stages execute in sequence. One API call. Every component is lattice-based or hash-based. No classical-only dependencies.
Every component is optimized for production throughput on Graviton4 ARM. No research prototypes. No toy demos. Real numbers, real hardware.
H33-128 BFV enables computation on encrypted data at scale. The data never leaves encryption — not during transit, not during processing, not ever.
Conservative parameter selection validated against the Homomorphic Encryption Standard. All operations in the polynomial ring Z[X]/(XN+1).
| Parameter | Value |
|---|---|
| Polynomial degree (N) | 4,096 |
| Ciphertext modulus (Q) | 56-bit single |
| Plaintext modulus (t) | 65,537 |
| Standard deviation (σ) | 3.2 |
| SIMD slots | 4,096 ÷ 128 = 32 per ciphertext |
| Security level | 128-bit (NIST Level 1) |
Choose the trust level your application requires. Each tier builds on the one below it. All tiers include full FHE computation.
curl -X POST https://api.h33.ai/v1/fhe/h33-128/encrypt \ -H "Authorization: Bearer $H33_API_KEY" \ -H "Content-Type: application/json" \ -d '{"plaintext": [1, 0, 1, 0, ...], "public_key_id": "pk_abc123"}'
dilithium_signature field.curl -X POST https://api.h33.ai/v1/fhe/h33-128/biometric-match \ -H "Authorization: Bearer $H33_API_KEY" \ -H "X-H33-Tier: dilithium" \ -d '{"ciphertext_id": "ct_xyz", "template_id": "tmpl_abc"}' # Response includes: # "dilithium_signature": "base64...", # "dilithium_public_key": "base64..."
curl -X POST https://api.h33.ai/v1/fhe/h33-128/biometric-match \ -H "Authorization: Bearer $H33_API_KEY" \ -H "X-H33-Tier: 3key" \ -d '{"ciphertext_id": "ct_xyz", "template_id": "tmpl_abc"}' # Response includes full 3-layer signature bundle: # "h33_3key_signature": "base64...", # "ed25519_pk": "base64...", # "dilithium_pk": "base64...", # "falcon_pk": "base64..."
Every H33-128 API call is powered by Noise Pilot — our AI-driven noise budget optimizer. It tracks, schedules, and optimizes FHE noise automatically so you never worry about ciphertext corruption or budget exhaustion.
N=4096 and 128-dimensional biometric vectors, we pack 4096 / 128 = 32 user templates into a single ciphertext. One FHE inner-product operation verifies all 32 users simultaneously in ~939 microseconds, which is why per-auth latency drops to roughly 36 microseconds.N=4096 and a 56-bit modulus, which provides 128-bit post-quantum security. This is the recommended baseline for most production workloads and the same level used by the NIST-standardized ML-KEM and ML-DSA algorithms.~35.25 microseconds per authentication. On Graviton4 with 96 workers running in parallel, this sustains over 2.21 million authentications per second.api.h33.ai. For dedicated deployments, we offer private instances on your preferred cloud provider./pricing to receive your API key instantly. No credit card required. The free tier has full access to all three trust tiers so you can test the complete pipeline before committing to a paid plan.Q that fits in a native 64-bit register. This eliminates CRT reconstruction overhead, reduces NTT transforms per operation (one modulus instead of many), and keeps the entire working set in L1 cache. The tradeoff is lower multiplicative depth, but biometric inner products only need depth 1, making single-modulus the optimal choice.BFV homomorphic encryption at 1.595 million auth/sec. Free tier included. No credit card required.