Privacy-Preserving Face Recognition With Multi-Edge Assistance for Intelligent Security Systems

Face recognition is one of the key technologies in intelligent security systems. Data privacy and identification efficiency have always been concerns about face recognition. Existing privacy-preserving protocols only focus on the training phase of face recognition. Since intelligent security systems mainly complete the calculation of large-scale face data in the identification phase, existing privacy-preserving protocols cannot be well applied to intelligent security systems. In this article, we propose the first privacy-preserving face recognition protocol for the calculations in the identification phase for intelligent security systems. We introduce the Householder matrix to blind user data including model data and face data, which enables the proposed protocol to support privacy-preserving face recognition on semi-trusted edge servers. Utilizing edge computing, fast response for large-scale face recognition can be achieved. The user can offload heavy calculations of matrix multiplication and Euclidean distances to edge servers simultaneously. The proposed protocol supports parallel computing based on multiple edge servers and thus enhances the efficiency of face recognition in intelligent security systems. Moreover, the recognition accuracy in the proposed protocol is the same as that in the original PCA-based face recognition algorithm. The security analysis demonstrates that the protocol protects the privacy of user data. The numerical analysis and simulation experiments are carried out to show the efficiency and feasibility of the proposed protocol.

I. Introduction

In the big data era, the rapid development of the Internet of Things (IoT) has introduced many emerging applications [1]. Intelligent technologies in the IoT have brought great changes to our daily life. Specifically, intelligent security systems are important IoT applications for maintaining public safety [2]. For example, for offender tracking and criminal investigations, public security organs deploy a lot of IoT devices, such as surveillance cameras and smart capture devices, in crowded places, including stations, airports, supermarkets, etc. They try to spot suspects who may be lurking in the crowd. For simplicity, we hereafter call the suspects who need to be caught as target objects. How to quickly find the target object in the dense crowd is a key issue for public security organs. Face recognition is a mainstream technology to solve this problem. The face images of target objects can be preprocessed to obtain the target face database. Public security organs collect face data from the crowd through deployed IoT devices. Then, they can match the collected face data with the target face database through face recognition technology.