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Privacy Enhanced Cloud-Based Facial Recognition

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Abstract

Homomorphic encryption is a significant method to protect user privacy in cloud computing environment. Due to the computation efficiency issue, there is still not many homomorphic encryption applications for common users. In this paper,we try to use homomorphic encryption to enhance the privacy in cloud-based face recognition system. By balancing the workload between client and server,and reimplementing the similarity measurement function, our homomorphic encryption version’s performance is almost the same as the original version in terms of accuracy and time consumption. Our work is especially beneficial to many face recognition methods that are using Euclidian distance as their similarity metric.

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Acknowledgements

The research is supported by National Key Research and Development Plan (2018YFB1404102),National Nature Science Foundation of China(U1609215),and Nature Science Foundation of Zhejiang Province(LQ20F020008).

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Authors and Affiliations

  1. Department of Information Security, Zhejiang Gongshang University, Hangzhou, China

    Tao Yang & Yuhang Zhang

  2. Department of Computer Science, Zhejiang Gongshang University, Hangzhou, China

    Jie Sun & Xun Wang

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  1. Tao Yang
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  2. Yuhang Zhang
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  3. Jie Sun
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  4. Xun Wang
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Correspondence to Jie Sun.

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Yang, T., Zhang, Y., Sun, J. et al. Privacy Enhanced Cloud-Based Facial Recognition. Neural Process Lett 54, 2717–2725 (2022). https://doi.org/10.1007/s11063-021-10477-y

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