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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

Indonesian

Pengenalan wajah telah menjadi pusat untuk sistem otentikasi modern, kerangka kerja keamanan, dan layanan yang dipersonalisasi. Namun, penerapannya yang luas terhalang oleh risiko privasi yang parah, terutama yang mengelilingi akses yang tidak sah ke data biometrik yang sensitif. Untuk mengatasi tantangan ini, makalah ini menyajikan GhostFaceMPC, sistem pengenalan wajah pertama dari ujung ke ujung, yang mempertahankan privasi untuk mengevaluasi ekstrak jaringan saraf konvolusi yang mendalam dan cocok di bawah komputasi multi-pihak yang aman. GhostFaceMPC memungkinkan pemrosesan data wajah yang aman di semua tahap proses pengenalan wajah, dari ekstraksi fitur dan penyimpanan yang cocok, tanpa mengekspos gambar mentah atau embeddings biometrik pada tahap apa pun. Kami memperkenalkan kuantizasi multi-pihak

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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