FHECipher: 프로그래머블 부트스트래핑, CKKS 텐서 연산 및 완벽한 암호화 AI 추론 엔진

FHECipher는 격자 기반 완전 동형 암호(FHE: CKKS, BFV, BGV, TFHE) 암호 시스템을 설계하고 확장하여 데이터가 처리되는 동안에도 수학적으로 완벽히 보호되는 제로 트러스트 기밀 클라우드 컴퓨팅을 지원합니다.

FHECipher 시스템 아키텍처

Core cryptographic topology and protocol interaction of fhecipher.com

Central System: FHE Compute Core - Lattice Ring Engine

Cryptographic Nodes

  • CKKS Scheme
  • TFHE Gate
  • Bootstrapping
  • GPU Coprocessor
  • Encrypted ML

Architectural Layers & Protocols

  • Mathematical Ring Learning With Errors (RLWE) (R_Q = Z_Q[X]/(X^N + 1) with Giant Moduli (up to 1200 bits)): Encoding floating-point vectors and integers into complex polynomial coefficients
  • FHE Scheme Implementations (CKKS / BFV / TFHE) (Approximate and Exact Homomorphic Arithmetic Engines): Evaluating tensor linear algebra (CKKS) and boolean logic gates (TFHE) over ciphertexts
  • Bootstrapping & Noise Management (Algorithmic Noise Refreshing for Arbitrary Circuit Depth): Resetting accumulated polynomial noise to enable infinite deep neural network evaluation
  • Hardware Acceleration & GPU Offload (NVIDIA CUDA / AMD ROCm / Custom ASIC FHE Accelerators): Accelerating 1024-bit integer NTTs to achieve real-time encrypted AI inference

Key Metrics: Zero-Leak (Memory Plaintext Exposure) | N = 65,536 (Polynomial Ring Dimension) | Hardware Accel (Bootstrapping Acceleration) | Indistinguishable (Ciphertext Security Guarantee)

암호학의 성배: 암호화된 데이터 위에서 연산하기

Classical encryption protects data in transit and at rest, but exposes plaintext in memory during processing. FHE leverages high-dimensional Ring Learning With Errors (RLWE) lattices to perform additions and multiplications directly on ciphertexts.

Core Engineering Areas

Technical Articles

Related Cluster Architecture Specifications