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Privacy-preserving face recognition with outsourced computation

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Abstract

Face recognition is one of the most important biometrics pattern recognitions, which has been widely applied in a variety of enterprise, civilian and law enforcement. The privacy of biometrics data raises important concerns, in particular if computations over biometric data is performed at untrusted servers. In previous work of privacy-preserving face recognition, in order to protect individuals’ privacy, face recognition is performed over encrypted face images. However, these results increase the computation cost of the client and the face database owners, which may enable face recognition not to be executed. Consequently, it would be desirable to reduce computation cost over sensitive biometric data in such environments. Currently, no secure techniques for outsourcing face biometric recognition are readily available. In this paper, we propose a privacy-preserving face recognition protocol with outsourced computation for the first time, which efficiently protects individuals’ privacy. Our protocol substantially improves the previous works in terms of the online computation cost by outsourcing large computation task to a cloud server who has large computing power. In particular, the overall online computation cost of the client and the database owner in our protocol is at most 1/2 of the corresponding protocol in the state-of-the-art algorithms. In addition, the client requires the decryption operations with only O(1) independent of M, where M is the size of the face database. Furthermore, the client can verify the correctness of the recognition result.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grant No. 11271003, the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20134410110003, High Level Talents Project of Guangdong, Guangdong Provincial Natural Science Foundation under Grant No. S2012010009950, the Project of Department of Education of Guangdong Province under Grant No. 2013KJ-CX0146, the Natural Science Foundation of Bureau of Education of Guangzhou under Grant No. 2012A004, the basic research major projects of Department of Education of Guangdong Province under Grant No. 2004KZDXM044, and the Guangzhou Zhujiang Science and Technology Future Fellow Fund under Grant No. 2012J2200094.

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Authors and Affiliations

  1. College of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China

    Can Xiang, Chunming Tang, Yunlu Cai & Qiuxia Xu

  2. Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes, Guangzhou University, Guangzhou, 510006, China

    Chunming Tang

  3. State Key Laboratory of Information Security, Beijing, 100093, China

    Chunming Tang

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  1. Can Xiang
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  2. Chunming Tang
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  3. Yunlu Cai
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  4. Qiuxia Xu
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Correspondence to Chunming Tang.

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Communicated by V. Loia.

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Xiang, C., Tang, C., Cai, Y. et al. Privacy-preserving face recognition with outsourced computation. Soft Comput 20, 3735–3744 (2016). https://doi.org/10.1007/s00500-015-1759-5

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