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Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption

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Abstract

Face recognition is playing an increasingly important role in present society, and suffers from the privacy leakage in plaintext. Therefore, a recognition system based on homomorphic encryption that supports privacy preservation is designed and implemented in this paper. This system uses the CKKS algorithm in the SEAL library, latest homomorphic encryption achievement, to encrypt the normalized face feature vectors, and uses the FaceNet neural network to learn on the image’s ciphertext to achieve face classification. Finally, face recognition in ciphertext is accomplished. After been tested, the whole process of extracting feature vectors and encrypting a face image takes only about 1.712s in the developed system. The average time to compare a group of images in ciphertext is about 2.06s, and a group of images can be effectively recognized within 30 degrees of face bias, with a recognition accuracy of 96.71%. Compared to the face recognition scheme based on the Advanced Encryption Standard encryption algorithm in ciphertext proposed by Wang et al. in 2019, our scheme improves the recognition accuracy by 4.21%. Compared to the image recognition scheme based on the Elliptical encryption algorithm in ciphertext proposed by Kumar S et al. in 2018, the total spent time in our system is decreased by 76.2%. Therefore, our scheme has better operational efficiency and practical value while ensuring the users’ personal privacy. Compared to the face recognition systems in plaintext presented in recent years, our scheme has almost the same level on recognition accuracy and time efficiency.

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References

  1. Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems[J]. Communications of the ACM, 21(2), 120–126.

    Article  MathSciNet  Google Scholar 

  2. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices[C]. Proceedings of the forty-first annual ACM symposium on Theory of computing, 1, 169–178.

    Article  MathSciNet  Google Scholar 

  3. Smart, N. P., & Vercauteren, F. (2010). Fully homomorphic encryption with relatively small key and ciphertext sizes[C], 420–443. Springer, Berlin, Heidelberg: International Workshop on Public Key Cryptography.

    MATH  Google Scholar 

  4. Van Dijk, M., Gentry, C., & Halevi, S., et al. (2010). Fully homomorphic encryption over the integers[C], 24-43,Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, Berlin, Heidelberg

  5. Sun, Y., Chen, Y., & Wang, X., et al. (2014). Deep learning face representation by joint identification-verification[C]. Advances in neural information processing systems, , 1988–1996

  6. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering[C]. Proceedings of the IEEE conference on computer vision and pattern recognition,, 815–823.

  7. Sun, Y., Wang, X., & Tang, X. (2015). Deeply learned face representations are sparse, selective, and robust[C]. Proceedings of the IEEE conference on computer vision and pattern recognition,, 2892–2900.

  8. Wen, Y., Zhang, K., Li, Z., et al. (2016). A discriminative feature learning approach for deep face recognition[C]. European conference on computer vision, Springer, 9911, 499–515.

    Google Scholar 

  9. Guo, Y., & Zhang, L. One-shot face recognition by promoting underrepresented classes[J], arXiv preprint arXiv:1707.05574(2017).

  10. Deng, J., Zhou, Y., & Zafeiriou, S. (2017). Marginal loss for deep face recognition[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,, 60–68.

  11. Chen, B., Deng, W., & Du, J. (2017). Noisy softmax: Improving the generalization ability of dcnn via postponing the early softmax saturation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,, 5372–5381.

  12. Zhang, X., Fang, Z., Wen, Y., et al. (2017). Range loss for deep face recognition with long-tailed training data[C]. Proceedings of the IEEE International Conference on Computer Vision,, 5409–5418.

  13. Liu, Y., Li, H., Wang, X. Rethinking feature discrimination and polymerization for large-scale recognition[J], arXiv preprint arXiv:1710.00870(2017).

  14. Wu, W., Kan, M., Liu, X., et al. (2017). Recursive spatial transformer (rest) for alignment-free face recognition[C]. Proceedings of the IEEE International Conference on Computer Vision,, 3772–3780.

  15. Liu, W., Wen, Y., Yu, Z., et al. (2017). Sphereface: Deep hypersphere embedding for face recognition[C]. Proceedings of the IEEE conference on computer vision and pattern recognition,, 212–220.

  16. Wang, X., Xue, H., Liu, X., et al. (2019). A privacy-preserving edge computation-based face verification system for user authentication[J]. IEEE Access, 7, 14186–14197.

    Article  Google Scholar 

  17. Wang, Y., & Nakachi, T. (2020). A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks[J]. IEEE Access, 8, 136056–136070.

    Article  Google Scholar 

  18. Jin, X., Han, Q., & Li, X., et al. (2020). Efficient blind face recognition in the cloud[J]. Multimedia Tools and Applications,, 1–18.

  19. Troncoso-Pastoriza, J. R., González-Jiménez, D., & Pérez-González, F. (2013). Fully private noninteractive face verification[J]. IEEE Transactions on Information Forensics and Security, 8, 1101–1114.

    Article  Google Scholar 

  20. Im, J. H., Jeon, S. Y., & Lee, M. K. (2020). Practical privacy-preserving face authentication for smartphones secure against malicious clients[J]. IEEE Transactions on Information Forensics and Security, 15, 2386–2401.

    Article  Google Scholar 

  21. Kumar, S., Singh, S. K., Singh, A. K., et al. (2018). Privacy preserving security using biometrics in cloud computing[J]. Multimedia Tools and Applications, 77, 11017–11039.

    Article  Google Scholar 

  22. Wan, W., & Lee, H.J. (2017). FaceNet Based Face Sketch Recognition[C], 432-436, 2017 International Conference on Computational Science and Computational Intelligence (CSCI 2017), IEEE, Las Vegas, NV, USA

  23. William, I., Rachmawanto, E.H., & Santoso, H.A., et al. (2019). Face recognition using facenet (Survey, Performance Test, and Comparison)[C], 1-6, 2019 Fourth International Conference on Informatics and Computing (ICIC), IEEE, Semarang, Indonesia

  24. , Cheon, J.H, & Stehlé, D. (2015). Fully homomophic encryption over the integers revisited[C], 513-536, Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, Berlin, Heidelberg(2015).

  25. Jäschke, A., & Armknecht, F. (2016). Accelerating homomorphic computations on rational numbers[C], 405-423, International Conference on Applied Cryptography and Network Security, Springer, Cham

  26. Dowlin, N., Gilad-Bachrach, R., Laine, K., et al. (2017). Manual for using homomorphic encryption for bioinformatics[J]. Proceedings of the IEEE, 105, 552–567.

    Google Scholar 

  27. Cheon, J.H., Kim, A., & Kim, M., et al. (2017). Homomorphic encryption for arithmetic of approximate numbers[C], 409-437, International Conference on the Theory and Application of Cryptology and Information Security, Springer, Cham

  28. Fan, J., & Vercauteren, F. (2012). Somewhat Practical Fully Homomorphic Encryption[J]. IACR Cryptol. ePrint Arch., 2012, 144.

    Google Scholar 

  29. Bajard, J.C., Eynard, J., Hasan, M.A., et al. (2016). A full RNS variant of FV like somewhat homomorphic encryption schemes[C], 423-442, International Conference on Selected Areas in Cryptography, Springer, Cham

  30. Cheon, J.H., Han, K., & Kim, A., et al. (2018). Bootstrapping for approximate homomorphic encryption[C], 360-384, Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, Cham

  31. Yatao, Y., Yang, Z., Qilin, Z., et al. (2020). Weighted electronic voting system with homomorphic encryption based on SEAL[J]. Chinese Journal of Computers, 043, 711–723.

    Google Scholar 

  32. Yatao, Y. A. N. G., & Yang, Z. H. A. O. (2021). Juanmei ZHANG, Jierun HUANG, Yuan GAO, recent development of theory and application on homomorphic encryption[J]. Journal of Electronics and Information Technology, 43, 475–487.

    Google Scholar 

  33. Melchor, C.A., Kilijian, M.O., & Lefebvre, C., et al. (2018). A comparison of the homomorphic encryption libraries HElib, SEAL and FV-NFLlib[C], 425-442, International Conference on Security for Information Technology and Communications, Springer, Cham

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Authors and Affiliations

  1. School of Telecommunication Engineering, Xidian University, Xi’an, 710071, China

    Yatao Yang & Qilin Zhang

  2. Beijing Electronics Science and Technology Institute, Beijing, 100070, China

    Yatao Yang, Qilin Zhang, Wenbin Gao, Chenghao Fan, Qinyuan Shu & Hang Yun

Authors
  1. Yatao Yang
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  2. Qilin Zhang
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  3. Wenbin Gao
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  4. Chenghao Fan
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  5. Qinyuan Shu
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  6. Hang Yun
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Correspondence to Yatao Yang.

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This work was supported by The State Cryptography Development Fund of Thirteen Five-year(MMJJ20170110).

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The authors declare that they have no Conflict of interest.

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Yang, Y., Zhang, Q., Gao, W. et al. Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption. Wireless Pers Commun 123, 3737–3754 (2022). https://doi.org/10.1007/s11277-021-09311-4

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  • DOI: https://doi.org/10.1007/s11277-021-09311-4

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