FHEID: 정보 유출 제로 생체 템플릿, 암호화된 임베딩 고속 검색 및 프라이버시 신원 증명

FHEID는 생체 신원 인식을 위한 완전 동형 암호(FHE) 프레임워크를 개척하여, 신뢰할 수 없는 클라우드 환경에서 암호화된 512차원 특징 벡터에 대해 유클리드 거리 및 코사인 유사도 매칭을 생체 템플릿 노출 없이 직접 실행합니다.

FHEID Homomorphic Biometric Pipeline & Cloud Matching Topology

Client biometric extraction, CKKS homomorphic encryption, and blind cloud similarity search

Central System: FHE Biometric Engine - Encrypted Vector Matching Core

Cryptographic Nodes

  • Client Feature Extractor
  • CKKS Homomorphic Encrypter
  • Untrusted Cloud Matcher
  • Encrypted Threshold Evaluator
  • Client Decision Decryptor

Architectural Layers & Protocols

  • Client Biometric Extraction Plane (Edge Neural Network / Secure Enclave): Extracting normalized 512-element floating-point feature embeddings from iris, face, or fingerprint sensors.
  • Homomorphic Vector Cryptosystem (CKKS Ring-LWE Homomorphic Encryption): Encrypting biometric embeddings into high-degree polynomial rings with configurable noise budgets.
  • Blind Cloud Evaluation Engine (SIMD Vector Batching & Rescaling Operations): Computing encrypted dot products across millions of stored encrypted templates in parallel without decryption.
  • Privacy Compliance & Audit Layer (GDPR Article 9 / CCPA / BIPA Biometric Shield): Guaranteeing mathematical impossibility of biometric template reconstruction from stolen databases.

Key Metrics: Zero Decryption (Cloud Biometric Privacy) | < 85 ms (Homomorphic Match Time) | 512 Dimensions (Vector Embedding Size) | 100% (GDPR / BIPA Compliance)

프라이버시를 보존하는 FHE 생체인증, 신뢰되지 않는 클라우드 엔클레이브에서의 암호화 홍채/지문 벡터 매칭

Authoritative technical engineering reference for privacy-preserving fhe biometric authentication, encrypted iris/fingerprint vector matching in untrusted cloud enclaves in NIST FIPS 203/204/205 standards, lattice-based cryptography, side-channel attack mitigation, and quantum-resilient infrastructure.

Core Engineering Areas

Technical Articles