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access icon free Improved chaff point generation for vault scheme in bio-cryptosystems

  • Author(s): Thi Hanh Nguyen 1, 2 ; Yi Wang 1, 3 ; Yajun Ha 3 ; Renfa Li 1
    • View affiliations
    • Affiliations: 1: School of Information Science and Engineering, Hunan University, ChangSha, Hunan, People's Republic of China;
      2: Faculty of Information Technology, Industry University of Ho Chi Minh City, Ho Chi Minh City, Vietnam;
      3: Department of Electrical and Computer Engineering, National University of Singapore, Singapore
  • Source: Volume 2, Issue 2, June 2013, p. 48 – 55
    DOI:  10.1049/iet-bmt.2012.0060 , Print ISSN 2047-4938, Online ISSN 2047-4946
© The Institution of Engineering and Technology
Received 10/10/2012, Accepted 08/04/2013, Revised 08/04/2013, Published

Fuzzy vault is the most practical scheme in bio-cryptosystems for the applications in protecting data in the real-world, error-prone environments. The biometric features were used to lock and unlock the secret key, which is encoded in the coefficients of a polynomial equation. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction problem. The vault performance can be enhanced by adding more noise (chaff) points to the vault. The existing methods for generating chaff points were time consuming as producing more than 200 chaff points. This paper proposes a new chaff point generation technique for the fuzzy vault in bio-cryptosystems which is less time-consuming for producing more than 200 points. Complexity study shows that our algorithm has a complexity of O(n 2), which is a significant improvement over the existing algorithm of the complexity of O(n 3). Our experimental results show that the proposed algorithm achieves 14.84 and 41.86 times faster than Clancy's and Khalil-Hani's algorithms in the case of generating 240 chaff points. To generate the same numbers of valid chaff points, our proposed method needs less candidate points than the existing methods. Our proposed algorithm generates 11% more chaff points compared to the Khalil-Hani's algorithm.

Inspec keywords: biometrics (access control); cryptography; polynomials

Other keywords: polynomial equation; fuzzy vault system; Clancy-Khalil-Hani algorithm; biometric features; bio-cryptosystems; data protection; vault scheme; polynomial reconstruction problem; chaff points generation module; secret key

Subjects: Algebra; Data security

References

    1. 1)
      • 16. Clancy, T.C., Kiyavash, N., Lin, D.J.: ‘Secure smartcard based fingerprint authentication’. Proc. ACM SIGMM Workshop on Biometrics Methods and Applications (WBMA), New York, NY, USA, 2003, pp. 45–52.
    2. 2)
      • 9. Juels, A., Wattenberg, M.: ‘A fuzzy commitment scheme’. Proc. Sixth ACM Conf. on Computer and Communications Security (CCS), New York, NY, USA, 1999, pp. 28–36.
    3. 3)
      • 2. Bhattacharyya, D., Ranjan, R., Farkhod Alisherov, A., Choi, M.: ‘Biometric authentication: a review’, Int. J. u- e-Serv. Sci. Technol., 2009, 2, (3), pp. 13–28.
    4. 4)
      • 3. Tuyls, P., Goseling, J.: ‘Capacity and examples of template-protecting biometric authentication systems’. ECCV Workshop BioAW, 2004, pp. 158–170.
    5. 5)
      • 12. Vielhauer, C., Steinmetz, R., Mayerhofer, A.: ‘Biometric hash based on statistical features of online signatures’. Sixtieth Int. Conf. on Pattern Recognition, 2002, vol. 1, pp. 123–126.
    6. 6)
      • 21. Li, P., Yang, X., Cao, K., Tao, X., Wang, R., Tian, J.: ‘An alignment-free fingerprint cryptosystem based on fuzzy vault scheme’, J. Netw. Comput. Appl., 2010, 33, (3), pp. 207–220 (doi: 10.1016/j.jnca.2009.12.003).
    7. 7)
      • 5. Boult, T.E., Scheirer, W.J., Woodworth, R.: ‘Revocable fingerprint biotokens: accuracy and security analysis’. IEEE Conf. on Computer Vision and Pattern Recognition, June 2007, pp. 1–8.
    8. 8)
      • 4. Nagar, A., Nandakumar, K., Jain, A.K.: ‘Securing fingerprint template: fuzzy vault with minutiae descriptors’. Int. Conf. on Pattern Recognition, December 2008, pp. 1–4.
    9. 9)
      • 1. Jain, A.K., Ross, A., Pankanti, S.: ‘Biometrics: a tool for information security’, IEEE Trans. Inf. Forensics Sec., 2006, 1, (2), pp. 125–143 (doi: 10.1109/TIFS.2006.873653).
    10. 10)
      • 14. Kanade, S., Petrovska-Delacretaz, D., Dorizzi, B.: ‘Generating and sharing biometrics based session keys for secure cryptographic applications’. Fourth IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), September 2010, pp. 1–7.
    11. 11)
      • 22. Nandakumar, K., Jain, A.K., Pankanti, S.: ‘Fingerprint-based fuzzy vault: implementation and performance’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (4), pp. 744–757 (doi: 10.1109/TIFS.2007.908165).
    12. 12)
      • 24. Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: ‘FVC2002: second fingerprint verification competition’. Proc. 60th Int. Conf. on Pattern Recognition, 2002, vol. 3, pp. 811–814.
    13. 13)
      • 6. Ratha, N.K., Chikkerur, S., Connell, J.H., Bolle, R.M.: ‘Generating cancelable fingerprint templates’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (4), pp. 561–572 (doi: 10.1109/TPAMI.2007.1004).
    14. 14)
      • 15. Uludag, U., Pankanti, S., Jain, A.K.: ‘Fuzzy vault for fingerprints’. Proc. Fifth Int. Conf. on Audio- and Video-Based Biometric Person Authentication (AVBPA), Springer, 2005(LNCS, 3546), pp. 310–319.
    15. 15)
      • 13. Dodis, Y., Reyzin, L., Smith, A.: ‘Fuzzy extractors: how to generate strong keys from biometrics and other noisy data’. Proc. Int. Conf. on Theory and Applications of Cryptographic Techniques, 2004, vol. 3027, pp. 523–540.
    16. 16)
      • 10. Juels, A., Sudan, M.: ‘A fuzzy vault scheme’. Proc. IEEE Int. Symp. on Information Theory, 2002, pp. 408.
    17. 17)
      • 23. Chang, E.-C., Shen, R., Teo, F.W.: ‘Finding the original point set hidden among chaff’. Proc. ACM Symp. on Information, Computer and Communications Security, Taipei, Taiwan, 2006, pp. 182–188.
    18. 18)
      • 11. Chang, Y.J., Zhang, W., Chen, T.: ‘Biometrics-based cryptographic key generation’. IEEE Int. Conf. on Multimedia and Expo (ICME), June 2004, vol. 3, pp. 2203–2206.
    19. 19)
      • 17. Khalil-Hani, M., Bakhteri, R.: ‘Securing cryptographic key with fuzzy vault based on a new chaff generation method’. Int. Conf. on High Performance Computing and Simulation (HPCS), July 2010, vol. 28, pp. 259–265.
    20. 20)
      • 18. Uludag, U., Jain, A.: ‘Securing fingerprint template: Fuzzy vault with helper data’. Proc. Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW), Washington, DC, USA, 2006, pp. 163.
    21. 21)
      • 19. Chung, Y., Moon, D., Lee, S., Jung, S., Kim, T., Ahn, D.: ‘Automatic alignment of fingerprint features for fuzzy fingerprint vault’. Proc. First SKLOIS conf. on Information Security and Cryptology (CISC), Springer, 2005(LNCS, 3822), pp. 358–369.
    22. 22)
      • 8. Teoh, A.B.J., Toh, K.-A., Yip, W.K.: ‘2^N Discretisation of biophasor in cancellable biometrics’. Proc. Int. Conf. on Advances in Biometrics, Berlin, Heidelberg, 2007, pp. 435–444.
    23. 23)
      • 7. Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.K.: ‘Cancelable biometric filters for face recognition’. Proc. Int. Conf. on Pattern Recognition, August 2004, vol. 3, pp. 922–925.
    24. 24)
      • 20. Li, P., Yang, X., Cao, K., Shi, P., Tian, J.: ‘Security-enhanced fuzzy fingerprint vault based on minutiae's local ridge information’. Proc. Third Int. Conf. on Advances in Biometrics (ICB), Berlin, Heidelberg, 2009, pp. 930–939.
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