VOIDFace：一种具有增强安全性的隐私保护多网络人脸识别
VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Security
AI总结：
本文提出VOIDFace框架，通过视觉秘密共享消除数据复制并增强数据控制，结合基于补丁的多训练网络，在VGGFace2数据集上实现了具备被遗忘权的隐私保护人脸识别，同时保持竞争力。
摘要：
机器学习技术的进步，结合大规模数据集的可用性，显著提高了人脸识别的准确性和效率。现代人脸识别系统使用从不同个体或公共存储库收集的大型人脸数据集进行训练。然而，为了训练，这些数据集经常被复制并存储在多个工作站中，导致数据复制，这使数据库管理和监督变得复杂。目前，一旦用户提交其人脸用于数据集准备，他们就失去了对其数据使用方式的控制，引发了重大的隐私和伦理问题。本文介绍了VOIDFace，一种用于人脸识别系统的新颖框架，解决了两个主要问题。首先，它消除了数据复制的需求，并通过使用视觉秘密共享来改善数据控制，以安全地存储训练人脸数据。其次，它提出了一种基于补丁的多训练网络，该网络使用这种新颖的训练数据存储机制来开发一个强大、保护隐私的人脸识别系统。通过整合这些进步，VOIDFace旨在提高人脸识别训练的隐私、安全性和效率，同时确保对敏感个人人脸数据有更大的控制权。VOIDFace还使用户能够行使他们的被遗忘权属性来控制他们的个人数据。在VGGFace2数据集上的实验评估表明，VOIDFace提供了被遗忘权、改善的数据控制、安全性和隐私，同时保持了有竞争力的人脸识别性能。代码可在以下网址获得：https://github.com/ajnasmuhammed89/VOIDFace
英文摘要：
Advancement of machine learning techniques, combined with the availability of large-scale datasets, has significantly improved the accuracy and efficiency of facial recognition. Modern facial recognition systems are trained using large face datasets collected from diverse individuals or public repositories. However, for training, these datasets are often replicated and stored in multiple workstations, resulting in data replication, which complicates database management and oversight. Currently, once a user submits their face for dataset preparation, they lose control over how their data is used, raising significant privacy and ethical concerns. This paper introduces VOIDFace, a novel framework for facial recognition systems that addresses two major issues. First, it eliminates the need of data replication and improves data control to securely store training face data by using visual secret sharing. Second, it proposes a patch-based multi-training network that uses this novel training data storage mechanism to develop a robust, privacy-preserving facial recognition system. By integrating these advancements, VOIDFace aims to improve the privacy, security, and efficiency of facial recognition training, while ensuring greater control over sensitive personal face data. VOIDFace also enables users to exercise their Right-To-Be-Forgotten property to control their personal data. Experimental evaluations on the VGGFace2 dataset show that VOIDFace provides Right-To-Be-Forgotten, improved data control, security, and privacy while maintaining competitive facial recognition performance. Code is available at: https://github.com/ajnasmuhammed89/VOIDFace
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
