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Efficient blind face recognition in the cloud

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Abstract

Nowadays, with the maturity and wide application of face recognition technology, the recognition accuracy, recognition efficiency, and data security have attracted people’s attention. However, when face recognition is performed, face information is completely exposed to the cloud server without any protection measures. Therefore, a series of problems caused by insecure face information is coming. Can we find a way to prevent uncontrolled use of facial information without cloud protection and improve recognition efficiency and accuracy? Given this situation, we have proposed two options. The first requires a third-party library; the second does not require a third-party library. The first scheme is efficient privacy preserving face identification in the cloud through sparse representation, which relies on the third-party face image database, and the first scheme is simply referred to as SRBased. The second scheme is efficient privacy preserving face identification in the cloud based on deep neural network, which does not depend on the third-party face image database, and the second scheme is simply referred to as DNNBased. Both schemes can be divided into two parts: client and cloud server. The client is responsible for acquiring face images, and the server is responsible for recognizing and calculating. Through homomorphic encryption and OT protocol, secure face recognition is realized. In the whole recognition process, the server does not need to decrypt the image data. In the two schemes, the client and the server will not get any information from each other. Even if the third party intercepts the ciphertext in the transmission process, it will not get any information under the premise of private key security. Therefore, the two schemes can achieve the purpose of protecting privacy and security. The experimental results show that the efficiency of the two schemes is greatly improved compared with SCiFI schemes. The second scheme improves recognition accuracy greatly.

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Acknowledgements

Parts of the results and figures presented in this paper have previously appeared in our previous work [2, 6]. We add more technical details and experimental results in this version. This work is partially supported by the National Natural Science Foundation of China (grant numbers 61772047, 61772513), Big Data Application on lmproving Government Governance Capabilities National Engineering Laboratory Open Fund Project (grant number W-2018022), the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety (grant number BTBD-2018KF-07), Beijing Technology and Business University, and the Fundamental Research Funds for the Central Universities (grant numbers. 328201903).

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Authors and Affiliations

  1. Department of Cyber Security, Beijing Electronic Science and Technology Institute, Beijing, 100070, China

    Xin Jin, Xiaodong Li & Hongbo Sun

  2. CETC Big Data Research Institute Co., Ltd., Guiyang, 550022, Guizhou, China

    Xin Jin, Xiaodong Li & Chuanqiang Wu

  3. Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory, Guiyang, 550022, China

    Xin Jin & Xiaodong Li

  4. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China

    Xin Jin, Xiaodong Li & Ruijun Liu

  5. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Qing Han

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  1. Xin Jin
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  2. Qing Han
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  3. Xiaodong Li
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  4. Chuanqiang Wu
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  5. Hongbo Sun
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  6. Ruijun Liu
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Correspondence to Xiaodong Li.

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Jin, X., Han, Q., Li, X. et al. Efficient blind face recognition in the cloud. Multimed Tools Appl 79, 12533–12550 (2020). https://doi.org/10.1007/s11042-019-08280-y

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  • DOI: https://doi.org/10.1007/s11042-019-08280-y

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