Skip to main content

A Practical Privacy-Preserving Face Authentication Scheme with Revocability and Reusability

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11337))

Abstract

Revocability and reusability are important properties in an authentication scheme in reality. The former requires that the user credential stored in the authentication server be easily replaced if it is compromised while the latter allows the credentials of the same user to appear independent in cross-domain applications. However, the invariable biometrics features in the face authentication poses a great challenge to accomplishing these two properties. Existing solutions either sacrifice the accuracy of the authentication result or rely on a trusted third party. In this paper, we propose a novel privacy-preserving face authentication scheme without the assistance of an additional server, which achieves both revocability and reusability as well as the same accuracy level of the plaintext face recognition that uses Euclidean distance measure. Moreover, we rigorously analyze the security of our scheme using the simulation technique and conduct the experiment on a real-world dataset to demonstrate its efficiency. We report that a successful user authentication costs less than a second on a smartphone with common specs.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Casia-webface-database. http://www.cbsr.ia.ac.cn/english/Databases.asp

  2. Smile to pay. https://www.antfin.com/report.htm. Accessed 16 Mar 2015

  3. Your face is your secure password. https://www.apple.com/iphone-x/#face-id

  4. Boyen, X.: Reusable cryptographic fuzzy extractors. In: Proceedings of the 11th ACM Conference on Computer and Communications Security. ACM (2004)

    Google Scholar 

  5. Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, Hoboken (2009)

    Book  Google Scholar 

  6. Canetti, R., Fuller, B., Paneth, O., Reyzin, L., Smith, A.: Reusable fuzzy extractors for low-entropy distributions. In: Fischlin, M., Coron, J.S. (eds.) EUROCRYPT 2016. LNCS, vol. 9665, pp. 117–146. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49890-3_5

    Chapter  Google Scholar 

  7. Cui, H., Au, M.H., Qin, B., Deng, R.H., Yi, X.: Fuzzy public-key encryption based on biometric data. In: Okamoto, T., Yu, Y., Au, M.H., Li, Y. (eds.) ProvSec 2017. LNCS, vol. 10592, pp. 400–409. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68637-0_24

    Chapter  Google Scholar 

  8. Dodis, Y., Reyzin, L., Smith, A.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 523–540. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24676-3_31

    Chapter  Google Scholar 

  9. Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 235–253. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03168-7_14

    Chapter  Google Scholar 

  10. Gunasinghe, H., Bertino, E.: PrivBioMTAuth: privacy preserving biometrics-based and user centric protocol for user authentication from mobile phones. IEEE Trans. Inf. Forensics Secur. 13(4), 1042–1057 (2018)

    Article  Google Scholar 

  11. Guo, F., Susilo, W., Mu, Y.: Distance-based encryption: how to embed fuzziness in biometric-based encryption. IEEE Trans. Inf. Forensics Secur. 11(2), 247–257 (2016)

    Article  Google Scholar 

  12. Li, J., Li, J., Chen, X., Jia, C., Lou, W.: Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans. Comput. 64(2), 425–437 (2015)

    Article  MathSciNet  Google Scholar 

  13. Li, J., Sun, L., Yan, Q., Li, Z., Srisa-an, W., Ye, H.: Significant permission identification for machine learning based android malware detection. IEEE Trans. Industr. Inf. 14, 3216–3225 (2018)

    Article  Google Scholar 

  14. Li, P., Li, T., Ye, H., Li, J., Chen, X., Xiang, Y.: Privacy-preserving machine learning with multiple data providers. Future Gener. Comput. Syst. 87, 341–350 (2018)

    Article  Google Scholar 

  15. Liu, K., Kargupta, H., Ryan, J.: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans. Knowl. Data Eng. 18(1), 92–106 (2006)

    Article  Google Scholar 

  16. Matsuda, T., Takahashi, K., Murakami, T., Hanaoka, G.: Fuzzy signatures: relaxing requirements and a new construction. In: Manulis, M., Sadeghi, A.R., Schneider, S. (eds.) ACNS 2016. LNCS, vol. 9696, pp. 97–116. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39555-5_6

    Chapter  MATH  Google Scholar 

  17. Ouyang, W., et al.: DeepID-Net: deformable deep convolutional neural networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  18. Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16

    Chapter  Google Scholar 

  19. Patel, V.M., Ratha, N.K., Chellappa, R.: Cancelable biometrics: a review. IEEE Signal Process. Mag. 32(5), 54–65 (2015)

    Article  Google Scholar 

  20. Ratha, N.K.: Privacy protection in high security biometrics applications. In: Kumar, A., Zhang, D. (eds.) ICEB 2010. LNCS, vol. 6005, pp. 62–69. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12595-9_9

    Chapter  Google Scholar 

  21. Sadeghi, A.R., Schneider, T., Wehrenberg, I.: Efficient privacy-preserving face recognition. In: Lee, D., Hong, S. (eds.) ICISC 2009. LNCS, vol. 5984, pp. 229–244. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14423-3_16

    Chapter  Google Scholar 

  22. Sun, Y., Liang, D., Wang, X., Tang, X.: Deepid3: face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873 (2015)

  23. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  24. Takahashi, K., Matsuda, T., Murakami, T., Hanaoka, G., Nishigaki, M.: A signature scheme with a fuzzy private key. In: Malkin, T., Kolesnikov, V., Lewko, A.B., Polychronakis, M. (eds.) ACNS 2015. LNCS, vol. 9092, pp. 105–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-28166-7_6

    Chapter  Google Scholar 

  25. Wu, Z., Liang, B., You, L., Jian, Z., Li, J.: High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia. Soft Comput. 20(12), 4907–4918 (2016)

    Article  Google Scholar 

  26. Wu, Z., Tian, L., Li, P., Wu, T., Jiang, M., Wu, C.: Generating stable biometric keys for flexible cloud computing authentication using finger vein. Inf. Sci. 433–434, 431–447 (2018)

    Article  Google Scholar 

  27. Xia, Z., Xiong, N.N., Vasilakos, A.V., Sun, X.: EPCBIR: an efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf. Sci. 387, 195–204 (2017)

    Article  Google Scholar 

  28. Zhuang, D., Wang, S., Chang, J.M.: FRiPAL: face recognition in privacy abstraction layer. In: 2017 IEEE Conference on Dependable and Secure Computing. IEEE (2017)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Key Research and Development Program of China under Grant 2016YFB0800601, the Key Program of NSFC-Tongyong Union Foundation under Grant U1636209 and the Key Basic Research Plan in Shaanxi Province under Grant 2017ZDXM-GY-014.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuefeng Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lei, J., Pei, Q., Liu, X., Sun, W. (2018). A Practical Privacy-Preserving Face Authentication Scheme with Revocability and Reusability. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05063-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05062-7

  • Online ISBN: 978-3-030-05063-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics