Improved chaff point generation for vault scheme in bio-cryptosystems

Improved chaff point generation for vault scheme in bio-cryptosystems

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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.


    1. 1)
      • 1. Jain, A.K., Ross, A., Pankanti, S.: ‘Biometrics: a tool for information security’, IEEE Trans. Inf. Forensics Sec., 2006, 1, (2), pp. 125143 (doi: 10.1109/TIFS.2006.873653).
    2. 2)
      • 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. 1328.
    3. 3)
      • 3. Tuyls, P., Goseling, J.: ‘Capacity and examples of template-protecting biometric authentication systems’. ECCV Workshop BioAW, 2004, pp. 158170.
    4. 4)
      • 4. Nagar, A., Nandakumar, K., Jain, A.K.: ‘Securing fingerprint template: fuzzy vault with minutiae descriptors’. Int. Conf. on Pattern Recognition, December 2008, pp. 14.
    5. 5)
      • 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. 18.
    6. 6)
      • 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. 561572 (doi: 10.1109/TPAMI.2007.1004).
    7. 7)
      • 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. 922925.
    8. 8)
      • 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. 435444.
    9. 9)
      • 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. 2836.
    10. 10)
      • 10. Juels, A., Sudan, M.: ‘A fuzzy vault scheme’. Proc. IEEE Int. Symp. on Information Theory, 2002, pp. 408.
    11. 11)
      • 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. 22032206.
    12. 12)
      • 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. 123126.
    13. 13)
      • 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. 523540.
    14. 14)
      • 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. 17.
    15. 15)
      • 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. 310319.
    16. 16)
      • 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. 4552.
    17. 17)
      • 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. 259265.
    18. 18)
      • 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.
    19. 19)
      • 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. 358369.
    20. 20)
      • 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. 930939.
    21. 21)
      • 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. 207220 (doi: 10.1016/j.jnca.2009.12.003).
    22. 22)
      • 22. Nandakumar, K., Jain, A.K., Pankanti, S.: ‘Fingerprint-based fuzzy vault: implementation and performance’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (4), pp. 744757 (doi: 10.1109/TIFS.2007.908165).
    23. 23)
      • 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. 182188.
    24. 24)
      • 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. 811814.

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