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GhostFaceMPC: Privacy-Preserving Face Recognition with Multi-Party Computation

Davide Carnemolla, D Catalano, Mario Di Raimondo · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29987 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

面部识别已成为现代身份验证系统,安全框架和个性化服务的核心。然而,其广泛采用受到严重的隐私风险的阻碍,特别是围绕着未经授权的访问敏感生物识别数据。为了应对这些挑战,本文介绍了GhostFaceMPC,第一个终端、隐私保护的面部识别系统,用于评估深度神经网络提取器并在安全的多方计算中匹配。GhostFaceMPC允许在面部识别过程的所有阶段安全地处理面部数据,从功能提取和存储到匹配,而不暴露任何阶段的原始图像或生物识别嵌入。我们引入了GhostFaceNet模型和电路优化,使面部识别在多方计算中实用。代码

Machine translation (Meta M2M100); the English original follows.

Abstract

Facial recognition has become central to modern authentication systems, security frameworks, and personalized services. However, its widespread adoption is hindered by severe privacy risks, particularly surrounding unauthorized access to sensitive biometric data. To address these challenges, this paper presents GhostFaceMPC, the first end-to-end, privacy-preserving face recognition system to evaluate a deep convolutional neural network extractor and matching under secure multi-party computation. GhostFaceMPC enables secure processing of facial data across all stages of a face recognition process, from feature extraction and storage to matching, without exposing raw images or biometric embeddings at any stage. We introduce a multi-party computation-friendly quantization of the GhostFaceNet model and circuit optimizations that make face recognition under multi-party computation practical. The code is available at https://github.com/crypto-unict/GhostFaceMPC.

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Topics: Computer science, Facial recognition system, Biometrics, Artificial intelligence, Convolutional neural network, Feature extraction

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