VOIDFace: A Privacy-Preserving Multi-Network Face Recognition With Enhanced Security

TL;DR

VOIDFace introduces a privacy-preserving facial recognition framework that eliminates data replication, enhances data control using visual secret sharing, and supports user rights like the Right-To-Be-Forgotten, while maintaining competitive accuracy.

Contribution

The paper presents a novel multi-network facial recognition system that leverages visual secret sharing to improve privacy and data control, addressing key ethical concerns in data management.

Findings

Maintains high recognition accuracy on VGGFace2 dataset.

Enables user control over personal data and supports the Right-To-Be-Forgotten.

Enhances security and privacy in facial recognition training processes.

Abstract

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