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MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud

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Abstract

In recent years, the extensive application of biometric identification has been witnessed in various fields, such as airport service, criminal investigation, counter-terrorism and so on. Due to the sensitivity of the biometric data, people’s concern over the leakage of their biometric data is a critical obstacle to hinder the future adoption of biometric identification applications. To address this problem, many schemes focusing on the privacy protection during biometric identification process have been proposed. However, identifying an individual in a huge database still faces many challenges while considering privacy protection and efficiency at the same time. In this paper, an efficient and privacy-preserving cloud based biometric identification scheme (named MASK) is proposed based on the M-tree data structure and symmetric homomorphic encryption (SHE) scheme. With MASK, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational cost of the cloud servers in searching the biometric dataset is significantly reduced. Besides, the accuracy of the identification service is not lost. Detailed security analysis shows that MASK can resist various known security threats. In addition, MASK is implemented and evaluated with a synthetic dataset and a real face dataset, and extensive simulation results demonstrate that MASK is efficient in terms of computational and communication costs.

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Acknowledgment

This work was supported in part by National Key Research and Development Program of China (2017YFB0802200), National Natural Science Foundation of China (61972304,61932015), Natural Science Foundation of Shaanxi Province (2019ZDLGY12-02),Technical Research program of the Public Security Ministry (2019JSYJA01).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, 710126, China

    Xiaopeng Yang, Hui Zhu, Fengwei Wang & Hui Li

  2. Faculty of Computer Science, University of New Brunswick, New Brunswick, E3B 5A3, Canada

    Xiaopeng Yang, Fengwei Wang, Songnian Zhang & Rongxing Lu

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  1. Xiaopeng Yang
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Correspondence to Hui Zhu.

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Yang, X., Zhu, H., Wang, F. et al. MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud. Peer-to-Peer Netw. Appl. 14, 2171–2186 (2021). https://doi.org/10.1007/s12083-021-01120-7

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