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Privacy protection based on binary fingerprint compression

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Abstract

This paper proposes a novel system to protect the fingerprint database based on compressed binary fingerprint images. In this system, the user can store private data with high capacity. First, the grayscale fingerprint image is transformed into a binary bitstream. Then, the binary bitstream is compressed using run-length encoding and Huffman encoding technique to create a sparse space to accommodate private data. Finally, the new image constructed by the obtained binary bitstream is encrypted with an encryption key. For fingerprint matching, it is only need to decrypt and decompress the encrypted image in the database to obtain the binary fingerprint image. When the matching is passed, the private data can be extracted to a user with data-embedding key. If the matching is failed, the private data cannot extracted for the purpose of privacy security. Even if a leakage of the encryption key occurs, this system can still protect the privacy data of the user due to the existence of the data-embedding key. Meanwhile, the encoding and decoding phases are real time, which guarantee the practicability of the proposed system.

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References

  1. Jain, A.K., Kumar, A.: Biometric recognition: an overview. In: Second generation biometrics: the ethical, legal and social context, pp. 49–79. Springer, New York (2012)

  2. Ratha, N.K., Connell, J.H., Bolle, R.M: Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001)

    Article  Google Scholar 

  3. Li, S., Kot, A.C.: Fingerprint combination for privacy protection. IEEE Trans. Inf. Forensic Secur. 8(2), 350–360 (2013)

    Article  Google Scholar 

  4. Li, S., Zhang, X.: Toward construction-based data hiding: from secrets to fingerprint images. IEEE Trans. Image Process. 28(3), 1482–1497 (2019)

    Article  MathSciNet  Google Scholar 

  5. Li, S., Zhang, X., Qian, Z., Feng, G., Ren, Y.: Key based artificial fingerprint generation for privacy protection. IEEE Trans. Dependable Secure Comput. https://doi.org/10.1109/TDSC.2018.2812192

  6. Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)

    Article  Google Scholar 

  7. Thanki, R., Borisagar, K.: Biometric watermarking technique based on CS theory and fast discrete curvelet transform for face and fingerprint protection. In: Advances in Signal Processing and Intelligent Recognition Systems, pp. 133–144. Springer, New York (2016)

  8. Sandhya, M., Prasad, M.V.N.K.: \(k\)-Nearest neighborhood structure (\(k\)-NNS) based alignment-free method for fingerprint template protection. In: 2015 International Conference on Biometrics (ICB), pp. 386–393. IEEE, New York (2015)

  9. Jin, Z., Teoh, A.B.J., Ong, T.S., Tee, C.: Fingerprint template protection with minutiae-based bit-string for security and privacy preserving. Expert Syst. Appl. 39(6), 6157–6167 (2012)

    Article  Google Scholar 

  10. Ferrara, M., Maltoni, D., Cappelli, R.: Noninvertible minutia cylinder-code representation. IEEE Trans. Inf. Forensics Secur. 7(6), 1727–1737 (2012)

    Article  Google Scholar 

  11. Ferrara, M., Maltoni, D., Cappelli, R.: A two-factor protection scheme for MCC fingerprint templates. In: Proceedings of the Lecture Notes in Informatics (LNI). Series of the Gesellschaft fur Informatik (GI), vol. 01, pp. 171–178 (2014)

  12. Choudhury, B., Then, P., Raman, V., Issac, B., Haldar, M.K.: Cancelable iris biometrics based on data hiding schemes. In: 2016 IEEE Student Conference on Research and Development (SCOReD), pp. 1–6. IEEE, New York (2016)

  13. Qin, C., Chang, C.-C., Chiu, Y.-P.: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans. Image Process. 23(3), 969–978 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Qin, C., Zhou, Q., Cao, F., Dong, J., Zhang, X.: Flexible lossy compression for selective encrypted image with image inpainting. IEEE Trans. Circuits Syst. Video Technol. https://doi.org/10.1109/TCSVT.2018.2878026

  15. Othman, A., Ross, A.: On mixing fingerprints. IEEE Trans. Inf. Forensics Secur. 8(1), 260–267 (2013)

    Article  Google Scholar 

  16. Li, C., Hu, J.: A security-enhanced alignment-free fuzzy vault-based fingerprint cryptosystem using pair-polar minutiae structures. IEEE Trans. Inf. Forensics Secur. 11(3), 543–555 (2016)

    Article  Google Scholar 

  17. Tao, J., Li, S., Zhang, X., Wang, Z.: Towards robust image steganography. IEEE Trans. Circuits Syst. Video Technol 29(2), 594–600 (2019)

    Article  Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  19. Golomb, S.: Run-length encodings (corresp.). IEEE Trans. Inf. Theory 12(3), 399–401 (1966)

    Article  MATH  Google Scholar 

  20. Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61602294, in part by the Shanghai Sailing Program under Grant 16YF1404100.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, People’s Republic of China

    Sheng Li, Jiajun Su, Zichi Wang & Xin Chen

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  1. Sheng Li
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  2. Jiajun Su
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  4. Xin Chen
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Correspondence to Zichi Wang.

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Li, S., Su, J., Wang, Z. et al. Privacy protection based on binary fingerprint compression. J Real-Time Image Proc 16, 791–798 (2019). https://doi.org/10.1007/s11554-019-00857-y

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