CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition

TL;DR

CipherFace is a framework that uses Fully Homomorphic Encryption to enable secure, privacy-preserving facial recognition in the cloud, allowing encrypted similarity calculations without exposing sensitive data.

Contribution

It introduces a novel encrypted distance computation method for facial recognition embeddings using FHE, enhancing privacy and cloud-based processing capabilities.

Findings

Demonstrates scalability with different models and embedding sizes

Shows effectiveness in real-world scenarios

Provides open-source implementation for community use

Abstract

Facial recognition systems rely on embeddings to represent facial images and determine identity by verifying if the distance between embeddings is below a pre-tuned threshold. While embeddings are not reversible to original images, they still contain sensitive information, making their security critical. Traditional encryption methods like AES are limited in securely utilizing cloud computational power for distance calculations. Homomorphic Encryption, allowing calculations on encrypted data, offers a robust alternative. This paper introduces CipherFace, a homomorphic encryption-driven framework for secure cloud-based facial recognition, which we have open-sourced at http://github.com/serengil/cipherface. By leveraging FHE, CipherFace ensures the privacy of embeddings while utilizing the cloud for efficient distance computation. Furthermore, we propose a novel encrypted distance computation method for both Euclidean and Cosine distances, addressing key challenges in performing secure similarity calculations on encrypted data. We also conducted experiments with different facial recognition models, various embedding sizes, and cryptosystem configurations, demonstrating the scalability and effectiveness of CipherFace in real-world applications.