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Reliable and privacy-preserving multi-instance iris verification using Paillier homomorphic encryption and one-digit checksum

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Abstract

The utilization of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially in recent years over traditional authentication systems. Since the biometric traits are irrevocable, two important issues such as security and privacy still need to be addressed in BAS. Researchers explore homomorphic encryption (HE) to propose several privacy-preserving BAS. However, the correctness of the evaluated results computed by the cloud server on the protected templates is still an open research challenge. These methods are able to conserve the privacy of biometric templates but unable to check the correctness of computed result results in false reject or accept. To overcome this issue, we suggest a reliable and privacy-preserving verifiable multi-instance iris verification system using Paillier HE and one-digit checksum (PVMIAPO). Modified local random projection is implemented on the fused iris template to produce the reduced template. Later, Paillier HE is applied on the reduced template to create the protected template. The result returned by the third party server is verified using the one-digit checksum. The efficiency of PVMIAPO is verified by experimenting with it on SDUMLA-HMT, IITD, and CASIA-V3-Interval iris databases. PVMIAPO gratifies the irreversibility, diversity, and revocability properties. PVMIAPO also obtains fair performance in contrast to the existing methods.

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Data availability

The dataset used in PVMIAPO to analyze its performance is available in the IITD [28], CASIA-V3-Interval, and SDUMLA-HMT [52]

Notes

  1. https://en.wikipedia.org/wiki/Inversive_congruential_generator.

References

  1. Abdellatef, E., Soliman, R.F., Omran, E.M., Ismail, N.A., Elrahman, S.E.A., Ismail, K.N., Rihan, M., Amin, M., Eisa, A.A., El-Samie, F.E.A.: Cancelable face and iris recognition system based on deep learning. Opt. Quant. Electron. 54(11), 702 (2022)

    Google Scholar 

  2. Acar, A., Aksu, H., Uluagac, A.S., Conti, M.: A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. (Csur) 51(4), 1–35 (2018)

    Google Scholar 

  3. Al-Saggaf, A.A.: A post-quantum fuzzy commitment scheme for biometric template protection: an experimental study. IEEE Access 9, 110952–110961 (2021)

    Google Scholar 

  4. Arjona, R., Baturone, I.: A post-quantum biometric template protection scheme based on learning parity with noise (lpn) commitments. IEEE Access 8, 182355–182365 (2020)

    Google Scholar 

  5. Bansal, V., Garg, S.: A cancelable biometric identification scheme based on bloom filter and format-preserving encryption. J. King Saud Univer.-Comput. Inform. Sci. 34(8), 5810–5821 (2022)

    Google Scholar 

  6. Cappelli, R., Maio, D., Lumini, A., Maltoni, D.: Fingerprint image reconstruction from standard templates. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1489–1503 (2007)

    Google Scholar 

  7. Dargan, S., Kumar, M.: A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Syst. Appl. 143, 113114 (2020)

    Google Scholar 

  8. Daugman, J.: How iris recognition works. In: The essential guide to image processing, Elsevier, pp 715–739 (2009)

  9. Delac, K., Grgic, M.: A survey of biometric recognition methods. In: Proceedings. Elmar-2004. 46th International symposium on electronics in marine, IEEE, pp 184–193 (2004)

  10. Eichenauer-Herrmann, J.: Inversive congruential pseudorandom numbers: A tutorial. International Statistical Review/Revue Internationale de Statistique pp 167–176 (1992)

  11. Gad, R., Talha, M., Abd El-Latif, A.A., Zorkany, M., Ayman, E.S., Nawal, E.F., Muhammad, G.: Iris recognition using multi-algorithmic approaches for cognitive internet of things (ciot) framework. Futur. Gener. Comput. Syst. 89, 178–191 (2018)

    Google Scholar 

  12. Galbally, J., Ross, A., Gomez-Barrero, M., Fierrez, J., Ortega-Garcia, J.: Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms. Comput. Vis. Image Underst. 117(10), 1512–1525 (2013)

    Google Scholar 

  13. Gasti, P., Šeděnka, J., Yang, Q., Zhou, G., Balagani, K.S.: Secure, fast, and energy-efficient outsourced authentication for smartphones. IEEE Trans. Inf. Forensics Secur. 11(11), 2556–2571 (2016)

    Google Scholar 

  14. Gayathri, M., Malathy, C.: Novel framework for multimodal biometric image authentication using visual share neural network. Pattern Recogn. Lett. 152, 1–9 (2021)

    Google Scholar 

  15. Gomez-Barrero, M., Rathgeb, C., Galbally, J., Busch, C., Fierrez, J.: Unlinkable and irreversible biometric template protection based on bloom filters. Inf. Sci. 370, 18–32 (2016)

    MathSciNet  Google Scholar 

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

    Google Scholar 

  17. Gomez-Barrero, M., Rathgeb, C., Li, G., Ramachandra, R., Galbally, J., Busch, C.: Multi-biometric template protection based on bloom filters. Inform. Fusion 42, 37–50 (2018)

    Google Scholar 

  18. Hammad, M., Liu, Y., Wang, K.: Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access 7, 26527–26542 (2018)

    Google Scholar 

  19. Heidari, H., Chalechale, A.: Biometric authentication using a deep learning approach based on different level fusion of finger knuckle print and fingernail. Expert Syst. Appl. 191, 116278 (2022)

    Google Scholar 

  20. Helmy, M., El-Rabaie, E.S.M., El-Dokany, I., Abd El-Samie, F.E.: A novel cancelable biometrics by rubik’s algorithm. Optik 285, 170475 (2022)

    Google Scholar 

  21. Helmy, M., El-Shafai, W., El-Rabaie, E.S.M., El-Dokany, I.M., Abd El-Samie, F.E.: A hybrid encryption framework based on rubik’s cube for cancelable biometric cyber security applications. Optik 258, 168773 (2022)

    Google Scholar 

  22. Hu, S., Li, M., Wang, Q., Chow, S.S., Du, M.: Outsourced biometric identification with privacy. IEEE Trans. Inf. Forensics Secur. 13(10), 2448–2463 (2018)

    Google Scholar 

  23. Jain, A., Bolle, R., Pankanti, S.: Biometrics: personal identification in networked society, vol. 479. Springer, Berlin (1999)

    Google Scholar 

  24. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Google Scholar 

  25. Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to biometrics. Springer, Berlin (2011)

    Google Scholar 

  26. Khodadoust, J., Medina-PÁrez, M.A., Monroy, R., Khodadoust, A.M., Mirkamali, S.S.: A multibiometric system based on the fusion of fingerprint, finger-vein, and finger-knuckle-print. Expert Syst. Appl. 176, 114687 (2021)

    Google Scholar 

  27. Kamlaskar, C., Deshmukh, S., Gosavi, S., Abhyankar, A.: Novel canonical correlation analysis based feature level fusion algorithm for multimodal recognition in biometric sensor systems. Sens. Lett. 17(1), 75–86 (2019)

    Google Scholar 

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

    Google Scholar 

  29. Kumar, M.M., Prasad, M.V., Raju, U.: Bmiae: blockchain-based multi-instance iris authentication using additive elgamal homomorphic encryption. IET Biometrics 9(4), 165–177 (2020)

    Google Scholar 

  30. Kumar, N.: Cbrc: a novel approach for cancelable biometric template generation using random permutation and chinese remainder theorem. Multim. Tools Appl. 81(16), 22027–22064 (2022)

    Google Scholar 

  31. Kumar, N.: Cbrw: a novel approach for cancelable biometric template generation based on 1-d random walk. Appl. Intell. 52(13), 15417–15435 (2022)

    Google Scholar 

  32. Lai, Y.L., Jin, Z., Teoh, A.B.J., Goi, B.M., Yap, W.S., Chai, T.Y., Rathgeb, C.: Cancellable iris template generation based on indexing-first-one hashing. Pattern Recogn. 64, 105–117 (2017)

    Google Scholar 

  33. Lee, M.J., Teoh, A.B.J., Uhl, A., Liang, S.N., Jin, Z.: A tokenless cancellable scheme for multimodal biometric systems. Comput. Security 108, 102350 (2021)

    Google Scholar 

  34. Lee, M.J., Jin, Z., Liang, S.N., Tistarelli, M.: Alignment-robust cancelable biometric scheme for iris verification. IEEE Trans. Inf. Forensics Secur. 17, 3449–3464 (2022)

    Google Scholar 

  35. Morampudi, M.K., Prasad, M.V., Raju, U.: Privacy-preserving iris authentication using fully homomorphic encryption. Multim. Tools Appl. 79(27), 19215–19237 (2020)

    Google Scholar 

  36. Morampudi, M.K., Prasad, M.V., Raju, U.: Privacy-preserving and verifiable multi-instance iris remote authentication using public auditor. Appl. Intell. 51(10), 6823–6836 (2021)

    Google Scholar 

  37. O’Gorman, L.: Comparing passwords, tokens, and biometrics for user authentication. Proc. IEEE 91(12), 2021–2040 (2003)

    Google Scholar 

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

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

    Google Scholar 

  40. Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Secur. Privacy 1(2), 33–42 (2003)

    Google Scholar 

  41. Punithavathi, P., Geetha, S., Sasikala, S.: Generation of cancelable iris template using bi-level transformation. In: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science, pp 94–100 (2017)

  42. Rathgeb, C., Uhl, A.: A survey on biometric cryptosystems and cancelable biometrics. EURASIP J. Inf. Secur. 1, 1–25 (2011)

    Google Scholar 

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

  44. Sadhya, D., Raman, B.: Generation of cancelable iris templates via randomized bit sampling. IEEE Trans. Inf. Forensics Secur. 14(11), 2972–2986 (2019)

    Google Scholar 

  45. Siddhad, G., Khanna, P.: Max-min threshold-based cancelable biometric templates for low-end devices. J. Electron. Imaging 31(3), 033025–033025 (2022)

    Google Scholar 

  46. Talreja, V., Valenti, M.C., Nasrabadi, N.M.: Deep hashing for secure multimodal biometrics. IEEE Trans. Inf. Forensics Secur. 16, 1306–1321 (2020)

    Google Scholar 

  47. Upmanyu, M., Namboodiri, A.M., Srinathan, K., Jawahar, C.: Blind authentication: a secure crypto-biometric verification protocol. IEEE Trans. Inf. Forensics Secur. 5(2), 255–268 (2010)

    Google Scholar 

  48. Vallabhadas, D.K., Sandhya, M.: Securing multimodal biometric template using local random projection and homomorphic encryption. J. Inform. Secur. Appl. 70, 103339 (2022)

    Google Scholar 

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

    Google Scholar 

  50. Vidya, B.S., Chandra, E.: Entropy based local binary pattern (elbp) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage. Alex. Eng. J. 58(1), 103–114 (2019)

    Google Scholar 

  51. Vijay, M., Indumathi, G.: Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications. J. Inform. Secur. Appl. 58, 102707 (2021)

    Google Scholar 

  52. Yin, Y., Liu, L., Sun, X.: Sdumla-hmt: a multimodal biometric database. In: Chinese conference on biometric recognition, pp. 260–268. Springer, Berlin (2011)

  53. Zhong, D., Shao, H., Du, X.: A hand-based multi-biometrics via deep hashing network and biometric graph matching. IEEE Trans. Inf. Forensics Secur. 14(12), 3140–3150 (2019)

    Google Scholar 

  54. Zhou, K., Ren, J.: Passbio: privacy-preserving user-centric biometric authentication. IEEE Trans. Inf. Forensics Secur. 13(12), 3050–3063 (2018)

    Google Scholar 

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering and Applied Sciences, Neerukonda-Kurugallu Village, Managalagiri Mandal, Guntur District, Andhra Pradesh, 522502, India

    Mahesh Kumar Morampudi, Sriramulu Bojjagani & Neeraj Kumar Sharma

  2. Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India

    Nagamani Gonthina

  3. Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, JNTUK, Kakinada, Andhra Pradesh, India

    Duggineni Veeraiah

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  1. Mahesh Kumar Morampudi
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  2. Nagamani Gonthina
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  4. Neeraj Kumar Sharma
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  5. Duggineni Veeraiah
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Contributions

MKM Conceptualization, Methodology, Software, Data curation, Writing - original draft, Writing - review & editing. NG: Methodology, Writing - review & editing. SB: Validation, Investigation, Writing-review & Editing. NKS: Validation, Investigation, Writing-review & Editing. DV: Validation, Writing-review & Editing.

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Correspondence to Mahesh Kumar Morampudi.

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Morampudi, M.K., Gonthina, N., Bojjagani, S. et al. Reliable and privacy-preserving multi-instance iris verification using Paillier homomorphic encryption and one-digit checksum. SIViP 18, 3723–3735 (2024). https://doi.org/10.1007/s11760-024-03036-0

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