Advertisement

Information Technology of Data Protection on the Basis of Combined Access Methods

  • Andrey Kupin
  • Yurii Kumchenko
  • Ivan Muzyka
  • Dennis Kuznetsov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

The main task of the article is to develop information technology (IT) for data protection based on combined access methods. The need to create a reliable IT for data protection is conditioned by an active increase in confidential information and unauthorized access. The article presents the existing static and dynamic biometric access methods, the evaluation of biometric technologies is reviewed: market segmentation, access errors and a general table of characteristics. A combined access method based on Acuity Market Intelligence and International Biometric Group data is proposed, which includes a combination of voice and face - a multimodal method. The article contains the calculation of the work accuracy by using the characteristic curves: DET (Detection error trade-off), which establish the relationship between FRR errors (False Rejection Rate) and FAR (False Acceptance Rate) and identify the advantages of a multimodal biometric personnel identification system comparing the unimodal one. Also, the mathematical model of IT for data protection has been developed. The proposed scheme of information links is developed for the IT for data protection based on combined access methods.

Keywords

Information technology Data protection Biometrics 

References

  1. 1.
    Tran, L.B., Le, T.H.: Person authentication using relevance vector machine (RVM) for face and fingerprint. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 7(5), 8–15 (2015).  https://doi.org/10.5815/ijmecs.2015.05.02CrossRefGoogle Scholar
  2. 2.
    Barde, S., Zadgaonkar, A.S., Sinha, G.R.: PCA based multimodal biometrics using ear and face modalities. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 6(5), 43–49 (2014).  https://doi.org/10.5815/ijitcs.2014.05.06CrossRefGoogle Scholar
  3. 3.
    Shankar, S., Udupi, V.R., Gavas, R.D.: Biometric verification, security concerns and related issues - a comprehensive study. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 8(4), 42–51 (2016).  https://doi.org/10.5815/ijitcs.2016.04.06CrossRefGoogle Scholar
  4. 4.
    Thakkar, D.: Top Five Biometrics: Face, Fingerprint, Iris, Palm and Voice. https://www.bayometric.com/biometrics-face-finger-iris-palm-voice. Accessed 17 Nov 2017
  5. 5.
    Malik, J., Girdhar,D., Dahiya, R., Sainarayanan, G.: Reference Threshold Calculation for Biometric Authentication. Int. J. Image Graph. Signal Process. (IJIGSP) 6(2), 46–53 (2014).  https://doi.org/10.5815/ijigsp.2014.02.06CrossRefGoogle Scholar
  6. 6.
    Voas, J.: NIST Special Publication 800-183. Networks of ‘Things’. http://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-183.pdf. Accessed 17 Nov 2017
  7. 7.
    Skliar, V.: NIST recommends: building blocks for describing IoT. https://habrahabr.ru/post/314956. Accessed 17 Nov 2017
  8. 8.
    Kupin, A., Kumchenko, Y.: Improved algorithm for creating a template for the information technology of biometric identification. Metall. Mining Ind. 7(4), 7–10 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Kryvyi Rih National UniversityKryvyi RihUkraine

Personalised recommendations