Selective Homomorphic Encryption With LLE Enhances Privacy and Scalability in Doorbell Face Recognition

Abstract

The rapid adoption of smart-home and Internet-of-Things (IoT) devices has intensified the need for privacy-preserving biometric authentication that is both secure and computationally efficient. This paper presents Hybrid-HE LLE, a practical framework that combines Locally Linear Embedding (LLE) with selective homomorphic encryption to protect face-recognition features in resource-constrained IoT environments. Unlike cloud-centric outsourcing, the proposed system performs all heavy linear-algebra operations within a semi-trusted Insider Hub, ensuring data sovereignty, low latency, and verifiable computation without revealing raw facial features. A sparse orthogonal or Toeplitz transform first obfuscates feature vectors, after which sensitive coefficients are selectively encrypted using CKKS-based polynomial encoding. Homomorphic hashing and optional zero-knowledge proofs guarantee the integrity and auditability of outsourced results. Experiments on the ORL and LFW datasets demonstrate over 94 % Rank-1 accuracy, while reducing client computation by 92 %, uplink bandwidth by 80 %, and energy usage by 55 %, with authentication latency below 120 ms on a Raspberry Pi 4-class edge device. The framework provides formal protection against IND-CPA, EUF-CMA, and IND-CCA adversaries and maintains compliance with GDPR/HIPAA requirements. Hybrid-HE LLE thus offers a scalable, secure, and real-time solution for privacy-preserving biometric access in modern IoT communication systems.