Top

Wireless Personal Communications

Published in:

05-11-2021

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

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

Published in: Wireless Personal Communications | Issue 4/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article A Scalable Block Chain Framework for User Identity Management in a Decentralized Network

next article Hybrid NOMA for Future Radio Access: Design, Potentials and Limitations

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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.MathSciNetCrossRef

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.MathSciNetCrossRef

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

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

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

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.

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.

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.

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.

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.

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

Title: Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption
Authors: Yatao Yang
Qilin Zhang
Wenbin Gao
Chenghao Fan
Qinyuan Shu
Hang Yun
Publication date: 05-11-2021
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-021-09311-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2022

OMAC: Optimal Medium Access Control Based Energy and QoS Compromise Techniques for Wireless Body Area Networks

Comparative Analysis of Ergodic Sum Capacity of Cooperative NOMA Aided with Spatial Modulation

Modeling and Simulation of Molecular Communication Based Nanonetwork Using Finite Shaped Spherical Receiver

C5: A Step Towards Smart World with Enhanced Holistic Wellbeing

Improved Measurement Matrix Construction with Pseudo-Random Sequence in Compressed Sensing

Journey of Artificial Intelligence