Abstract
At present, artificial intelligence technology is widely used in society, and various intelligent systems emerge as the times require. Due to the uniqueness of biometrics, most intelligent systems use biometric-based recognition technology, among which face recognition is the most widely used. To improve the security of intelligent system, this paper proposes a face authentication system based on edge computing and innovatively extracts the features of face image by convolution neural network, verifies the face by cosine similarity, and introduces a user privacy protection scheme based on secure nearest neighbor algorithm and secret sharing homomorphism technology. The results show that when the threshold is 0.51, the correct rate of face verification reaches 92.46%, which is far higher than the recognition strength of human eyes. In face recognition time consumption and recognition accuracy, the encryption scheme is basically consistent with the recognition time consumption in plaintext state. It can be seen that the security of the intelligent system with this scheme can be significantly improved. This research provides a certain reference value for the research on the ways to improve the security of intelligent system.
Highlights
With the rapid development of mobile network, multimedia data on network edge devices are increasing rapidly. e network communication load and storage space of the traditional cloud computing intelligent system are impacted
To improve the security of user identity authentication in intelligent system, an identity authentication scheme based on edge computing is proposed. e original face image is processed by convolution neural network, and the feature vector of face is extracted. e user identity registration technology based on secure nearest neighbor algorithm and the user identity authentication technology based on secret sharing homomorphism are introduced
Incompatibility, direct friendliness, and other characteristics of face recognition, it has become an authentication method in a variety of intelligent systems, and its security directly determines the security of intelligent systems. erefore, this paper proposes a privacy protection technology in an intelligent face authentication system based on edge computing [22]. e main technologies of face recognition include face detection, face data preprocessing, face feature extraction, similarity measurement, and discriminant classification, and output the recognition results [23]
Summary
With the rapid development of mobile network, multimedia data on network edge devices are increasing rapidly. e network communication load and storage space of the traditional cloud computing intelligent system are impacted. A lot of research has been carried out in edge computing, secure nearest neighbor algorithm, face recognition, intelligent system, user data privacy protection, and so on. In the aspect of improving the security of intelligent system, there is still a lack of research on using edge computing, convolutional neural network face feature extraction, and secure nearest neighbor algorithm to improve the security of face recognition. This paper proposes an intelligent system security enhancement scheme based on edge computing, which uses convolution neural network to extract the feature vector of face image and uses secure nearest neighbor algorithm to protect the user privacy. Combined with the encryption key M1, M2, (MT1 fia, MT2 fib) can be obtained as the feature vector for encryption in the privacy protection scheme. e fluorite protection scheme based on the secure nearest neighbor algorithm makes a lightweight encryption
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