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Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

Liu Jiasen, Wang Xu An, Chen Bowei, Tu Zheng, Zhao Kaiyang
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 17
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781799860105|DOI: 10.4018/IJMCMC.2021070103
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MLA

Jiasen, Liu, et al. "Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing." IJMCMC vol.12, no.3 2021: pp.27-43. http://doi.org/10.4018/IJMCMC.2021070103

APA

Jiasen, L., An, W. X., Bowei, C., Zheng, T., & Kaiyang, Z. (2021). Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 12(3), 27-43. http://doi.org/10.4018/IJMCMC.2021070103

Chicago

Jiasen, Liu, et al. "Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 12, no.3: 27-43. http://doi.org/10.4018/IJMCMC.2021070103

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Abstract

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.

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