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A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing

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Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14510))

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Abstract

Face recognition technology is widely used in various fields, such as law enforcement, payment systems, transportation, and access control. Traditional face authentication systems typically establish a facial feature template database for identity verification. However, this approach poses various security risks, such as the risk of plaintext feature data stored in cloud databases being leaked or stolen. To address these issues, in recent years, a face recognition technology based on homomorphic encryption has gained attention. Based on homomorphic encryption, face recognition can encrypt facial feature values and achieve feature matching without exposing the feature information. However, due to the encryption, face recognition in the ciphertext domain often requires considerable time. In this paper, we introduce the big data stream processing engine Flink to achieve parallel computation of face recognition in the ciphertext domain based on homomorphic encryption. We analyze the security, accuracy, and acceleration of this approach. Ultimately, we verify that this approach achieves recognition accuracy close to plaintext and significant efficiency improvement.

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Author information

Authors and Affiliations

  1. Fuzhou University, Fuzhou, China

    Gong Wang, Xianghan Zheng & Weipeng Xie

  2. Fujian Chuanzheng Communicatians College, Fuzhou, China

    Lingjing Zeng

Authors
  1. Gong Wang
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  2. Xianghan Zheng
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  3. Lingjing Zeng
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  4. Weipeng Xie
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Corresponding author

Correspondence to Weipeng Xie .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, G., Zheng, X., Zeng, L., Xie, W. (2024). A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_4

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  • DOI: https://doi.org/10.1007/978-981-99-9788-6_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9787-9

  • Online ISBN: 978-981-99-9788-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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