Research on Privacy Protection Technology in Face Identity Authentication System Based on Edge Computing

In today's society, the rapid development of the Internet makes People's Daily life become more intelligent and diversified. Today's society has entered a multifaceted era where everything is interconnected. Artificial intelligence technology is gradually replacing some traditional human services, such as intelligent robot customer service instead of traditional human customer service, intelligent face scanning security check in railway stations instead of traditional manual ticket checking, unmanned supermarket automatic checkout has liberated some social labor costs. All these changes are the result of the development of artificial intelligence technology in today's society. In recent years, unicorn startups focused on biometrics have sprung up all around us, such as BTU and its MEG VII (Face ++). Thanks to the development of Internet and artificial intelligence technology, in many application fields, the traditional access control and identity authentication technology based on password verification is gradually transforming to the scheme based on biometric identification verification. Secure identity authentication is very important to the application of Internet. Face recognition is the most popular technology among all biometric identification technologies. In the field of biometric identification technology, it has become the most widely used technology in the field of identity authentication because of its unique non-invasive, support for infrared and visible light, no need for user cooperation and many other advantages. In the field of education, examinee identification, pedestrian identification detection at the entrance of railway stations, face electronic payment, intelligent video surveillance system, intelligent attendance and access control system, intelligent unmanned supermarkets and customs clearance ports become the pioneer fields of face recognition applications. It can be seen that the era of “national face brushing” has arrived, and the application of face recognition technology will only be more and more widespread in the current era and in the future. However, due to the sensitivity of biometric data and the heterogeneity and openness of network environment, the privacy leakage of biometric data is difficult to avoid. At present, fog computing and edge computing have been paid more and more attention in many fields. In the case that cloud service providers are unable to provide sufficient security, edge computing shows its advantages. In this paper, mobile edge computing is introduced for the first time into the face privacy protection identity authentication system based on cloud server outsourcing computing. It can not only greatly reduce the interaction frequency between users and cloud server, improve the availability and fault tolerance of the system, but also contribute to the implementation of privacy protection scheme. A deep constitutional neural network for face feature extraction is trained using deep learning framework Cafe. Cosine similarity is used to complete face verification. A privacy protection scheme based on the secure nearest neighbor algorithm is proposed, which can not only protect the security of the face feature data at the edge computing node, but also allow the edge computing node to complete the face recognition operation against the encrypted face feature data. In addition, the encryption scheme does not require large computing resources, and the accuracy of face recognition in cipher text is exactly the same as that in explain. At present, most of the solutions either have high computational complexity or poor security performance. How to reduce the computational complexity and improve the real-time performance of the system while ensuring the high security of the private data has important research significance and value. Therefore, in the cloud server outsourcing computing environment, how to complete biometric identification on the premise of protecting the privacy of biological data has become a research hot spot.