Skip to main content
SpringerLink
Log in

Multi-instance iris remote authentication using private multi-class perceptron on malicious cloud server

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In recent years, biometric authentication system (BAS) has become the most promising and popular authentication system in identity management. Due to its capability to solve the limitations of unimodal systems, multi-biometric systems (MBS) have been extensively accepted in various fields. The main step in MBS is information fusion. On the other hand, directly storing the fused templates into a centralized server leads to privacy concerns. Recently, many BAS based on homomorphic encryption has been introduced to provide confidentiality for the fused templates. However, most of the existing solutions rely on an implication of the assumption that the server is “Honest-but-Curious”. As a result, the compromise of such server results into entire system vulnerability. To address this, we propose a novel P rivacy P reserving (PP) multi-instance iris remote authentication system to accord with attacks at the malicious server and over the transmission channel. Our scheme uses F ully H omomorphic E ncryption (FHE) to achieve the confidentiality of the fused iris templates and polynomial factorization algorithm to achieve the integrity of the matching result. We propose a PP iris authentication system using P rivate M ulti-C lass P erceptron (PMCP) by using the properties of FHE. Moreover, we propose C ontradistinguish S imilarity A nalysis (CSA), a feature level fusion technique that minimizes the between-class correlations and maximizes the pair-wise correlations. Our method has experimented on IITD and CASIA-V3-Interval iris databases to check the effectiveness and robustness. Experimental results show that our method provides improved accuracy, and eliminates the need to trust the cloud server when compared to the state-of-the-art approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.tribuneindia.com/news/nation/rs-500-10-minutes-and-you-have//-access-to-billion-aadhaar-details/523361.htmlhttp://www.tribuneindia.com/news/nation/rs-500-10-minutes-and-you-have//-access-to-billion-aadhaar-details/523361.html

  2. https://www.reuters.com/article/us-usa-cybersecurity-fingerprints/5-6-million-fingerprints-stolen-in-u-s-personnel//-data-hackgovernment-idUSKCN0RN1V820150923https://www.reuters.com/article/us-usa-cybersecurity-fingerprints/5-6-million-fingerprints-stolen-in-u-s-personnel//-data-hackgovernment-idUSKCN0RN1V820150923http://www.tribuneindia.com/news/nation/rs-500-10-minutes-and-you-have//-access-to-billion-aadhaar-details/523361.html

  3. http://biometrics.idealtest.org/dbDetailForUser.do?id=4

  4. https://www.shoup.net/ntl/

References

  1. Daugman J (2004) How iris recognition works. IEEE Trans Cir and Sys for Video Technol 14(1):21–30. https://doi.org/10.1109/TCSVT.2003.818350

    Article  Google Scholar 

  2. Thakkar D (2019) Applications of iris recognition system. https://www.bayometric.com/biometric-iris-recognition-application/, Accessed: 10-04-2019

  3. Ross A, Jain A (2004) Multimodal biometrics: an overview. In: Proceedings of 12th European signal processing conference, pp 1221–1224

  4. Ross AA, Nandakumar K, Jain AK (2006) Handbook of multibiometrics, vol 6. Springer

  5. Daugman J, Downing C (2001) Epigenetic randomness, complexity and singularity of human iris patterns. Proc Biol Sci/ R Soc 268(1477):1737–40

    Article  Google Scholar 

  6. Snoek CGM, Worring M, Smeulders AWM (2005) Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on multimedia, MULTIMEDIA ’05. ACM, New York, pp 399–402, DOI https://doi.org/10.1145/1101149.1101236, (to appear in print)

  7. Liu C, Wechsler H (2001) A shape- and texture-based enhanced fisher classifier for face recognition. IEEE Trans Image Proc: Publ IEEE Signal Process Soc 10(4):598–608. https://doi.org/10.1109/83.913594

    Article  MATH  Google Scholar 

  8. Yang J, Jy Yang, Zhang D, Jf L u (2003) Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn 36(6):1369– 1381

    Article  MATH  Google Scholar 

  9. Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS (2005) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448

    Article  Google Scholar 

  10. Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans Inform Forens Secur 11(9):1984–1996

    Article  Google Scholar 

  11. Bharadi V, Shah D, Thapa N, Pandya BH, Cosma G (2018) Multi-instance iris recognition. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–6

  12. Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TA (2018) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Applic 21(3):783–802

    Article  MathSciNet  Google Scholar 

  13. Rathgeb C, Tams B, Wagner J, Busch C (2016) Unlinkable improved multi-biometric iris fuzzy vault. EURASIP J Inf Secur 2016(1):26

    Article  Google Scholar 

  14. Barpanda SS, Sa PK, Marques O, Majhi B, Bakshi S (2018) Iris recognition with tunable filter bank based feature. Multimed Tools Appl 77(6):7637–7674

    Article  Google Scholar 

  15. Sardar M, Mitra S, Shankar BU (2018) Iris localization using rough entropy and csa: a soft computing approach. Appl Soft Comput 67:61–69

    Article  Google Scholar 

  16. Arsalan M, Kim DS, Lee MB, Owais M, Park KR (2019) Fred-net: fully residual encoder–decoder network for accurate iris segmentation. Expert Syst Appl 122:217–241

    Article  Google Scholar 

  17. Zhao Z, Kumar A (2019) A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features. Pattern Recogn 93:546–557

    Article  Google Scholar 

  18. Noruzi A, Mahlouji M, Shahidinejad A (2019) Iris recognition in unconstrained environment on graphic processing units with cuda. Artif Intell Rev, 1–25

  19. Venugopalan S, Savvides M (2011) How to generate spoofed irises from an iris code template. IEEE Trans Inform Forens Secur 6(2):385–395

    Article  Google Scholar 

  20. Jain AK, Nandakumar K, Nagar A (2008) Biometric template security. EURASIP J Adv Signal Process 2008:113

    Article  Google Scholar 

  21. Maiorana E, Hine GE, Campisi P (2014) Hill-climbing attacks on multibiometrics recognition systems. IEEE Trans Inform Forens Secur 10(5):900–915

    Article  Google Scholar 

  22. Prabhakar S, Pankanti S, Jain AK (2003) Biometric recognition: security and privacy concerns. IEEE Secur Privac 2:33–42

    Article  Google Scholar 

  23. Rathgeb C, Uhl A (2011) A survey on biometric cryptosystems and cancelable biometrics. EURASIP J Inf Secur 2011(1):3

    Article  Google Scholar 

  24. Patel VM, Ratha NK, Chellappa R (2015) Cancelable biometrics: a review. IEEE Signal Process Mag 32(5):54–65

    Article  Google Scholar 

  25. Fontaine C, Galand F (2007) A survey of homomorphic encryption for nonspecialists. EURASIP J Inf Secur 2007:15

    Google Scholar 

  26. Blanton M, Gasti P (2011) Secure and efficient protocols for iris and fingerprint identification. In: European symposium on research in computer security. Springer, pp 190–209

  27. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: International conference on the theory and applications of cryptographic techniques. Springer, pp 223–238

  28. Gomez-Barrero M, Maiorana E, Galbally J, Campisi P, Fierrez J (2017) Multi-biometric template protection based on homomorphic encryption. Pattern Recogn 67:149–163

    Article  Google Scholar 

  29. Alberto Torres WA, Bhattacharjee N, Srinivasan B (2015) Privacy-preserving biometrics authentication systems using fully homomorphic encryption. Int J Pervasive Comput Commun 11(2):151–168

    Article  Google Scholar 

  30. Yasuda M, Shimoyama T, Kogure J, Yokoyama K, Koshiba T (2015) New packing method in somewhat homomorphic encryption and its applications. Secur Commun Netw 8(13):2194– 2213

    Article  MATH  Google Scholar 

  31. Sun X, Zhang P, Liu JK, Yu J, Xie W (2019) Private machine learning classification based on fully homomorphic encryption. IEEE Trans Emerg Topics Comput, 1–1. https://doi.org/10.1109/TETC.2018.2794611

  32. Liu J, Huang K, Rong H, Wang H, Xian M (2015) Privacy-preserving public auditing for regenerating-code-based cloud storage. IEEE Trans Inform Forens Secur 10(7):1513–1528

    Article  Google Scholar 

  33. Graepel T, Lauter K, Naehrig M (2012) Ml confidential: machine learning on encrypted data. In: International conference on information security and cryptology. Springer, pp 1–21

  34. Setty ST, McPherson R, Blumberg AJ, Walfish M (2012) Making argument systems for outsourced computation practical (sometimes). NDSS 1:17

    Google Scholar 

  35. Gennaro R, Gentry C, Parno B (2010) Non-interactive verifiable computing: outsourcing computation to untrusted workers. In: Annual cryptology conference. Springer, pp 465–482

  36. Gennaro R, Wichs D (2013) Fully homomorphic message authenticators. In: International conference on the theory and application of cryptology and information security. Springer, pp 301–320

  37. Libert B, Peters T, Joye M, Yung M (2015) Linearly homomorphic structure-preserving signatures and their applications. Des Codes Crypt 77(2-3):441–477

    Article  MathSciNet  MATH  Google Scholar 

  38. Fan J, Vercauteren F (2012) Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 2012: 144

    Google Scholar 

  39. Rathgeb C, Uhl A, Wild P, Hofbauer H (2016) Design decisions for an iris recognition sdk. In: Handbook of iris recognition. Springer, pp 359–396

  40. Smart NP, Vercauteren F (2014) Fully homomorphic simd operations. Des Codes Cryptogr 71(1):57–81

    Article  MATH  Google Scholar 

  41. Brakerski Z, Gentry C, Halevi S (2013) Packed ciphertexts in lwe-based homomorphic encryption. In: International workshop on public key cryptography. Springer, pp 1–13

  42. Barni M, Failla P, Lazzeretti R, Sadeghi AR, Schneider T (2011) Privacy-preserving ecg classification with branching programs and neural networks. IEEE Trans Inform Forens Secur 6(2): 452–468

    Article  Google Scholar 

  43. Orlandi C, Piva A, Barni M (2007) Oblivious neural network computing via homomorphic encryption. EURASIP J Inform Secur 2007(1):037343

    Article  Google Scholar 

  44. Gentry C, Halevi S, Smart NP (2012) Fully homomorphic encryption with polylog overhead. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 465–482

  45. Anderson DP, Cobb J, Korpela E, Lebofsky M, Werthimer D (2002) Seti@ home: an experiment in public-resource computing. Commun ACM 45(11):56–61

    Article  Google Scholar 

  46. 24745:2011 I (2018) Iso/iec 24745:2011 - information technology – security techniques – biometric information protection. https://www.iso.org/standard/52946.html

  47. Kumar A, Passi A (2010) Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn 43(3):1016–1026

    Article  MATH  Google Scholar 

  48. Lyubashevsky V, Peikert C, Regev O (2010) On ideal lattices and learning with errors over rings. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 1–23

  49. Mosca M (2018) Cybersecurity in an era with quantum computers: will we be ready? IEEE Secur Privacy 16 (5):38–41

    Article  Google Scholar 

  50. Regev O (2010) The learning with errors problem. Invited survey in CCC, 7

  51. Martínez Díaz M, Fiérrez J, Alonso Fernández F, Ortega-García J, Sigüenza JA (2006) Hill-climbing and brute-force attacks on biometric systems: a case study in match-on-card fingerprint verification. In: Proceedings-international Carnahan conference on security technology. IEEE, pp 151–159

  52. Hadid A, Evans N, Marcel S, Fierrez J (2015) Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag 32(5):20–30

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Kim Laine, Senior Researcher, Microsoft for providing SEAL. We also thank Vishnu Naresh Boddeti, Assistant Professor, Michigan State University, for his valuable suggestions. We also thank Indian Institute of Technology Delhi (IITD), Centre for Biometrics and Security Research for providing access to their iris databases.

Author information

Authors and Affiliations

  1. Centre for Affordable Technologies Lab, Institute for Development and Research in Banking Technology, Road No.1, Castle Hills, Masab Tank, Hyderabad, 500057, India

    Mahesh Kumar Morampudi & Munaga V. N. K. Prasad

  2. National Institute of Technology, Warangal, 506004, Telangana, India

    Mahesh Kumar Morampudi & U. S. N. Raju

  3. Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Sowmya Veldandi

Authors
  1. Mahesh Kumar Morampudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sowmya Veldandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Munaga V. N. K. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. U. S. N. Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahesh Kumar Morampudi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morampudi, M.K., Veldandi, S., Prasad, M.V.N.K. et al. Multi-instance iris remote authentication using private multi-class perceptron on malicious cloud server. Appl Intell 50, 2848–2866 (2020). https://doi.org/10.1007/s10489-020-01681-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01681-9

Keywords

Search

Navigation