Kabirat B. Olayemi

Work place: Department of Computer Science, University of Ibadan, Oyo State, Nigeria

E-mail: kabiratolayemi@gmail.com


Research Interests: Information-Theoretic Security, Data Structures and Algorithms, Network Security, Information Security, Natural Language Processing, Computational Learning Theory


Kabirat B. Olayemi is a PhD research student at Federal University, Oye Ekit, Ekiti State. She has a B.Sc degree in Computer Science from Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria. She received her M.Sc degree in computer science from University of Ibadan, Oyo State, Nigeria. Her research interest includes Biometric security, Natural Language Processing (NLP), Machine learning and Internet security. She is a member of Data Science Nigeria.

Author Articles
A Fingerprint Template Protection Scheme Using Arnold Transform and Bio-hashing

By Olufade F. W. Onifade Kabirat B. Olayemi Folasade O. Isinkaye

DOI: https://doi.org/10.5815/ijigsp.2020.05.03, Pub. Date: 8 Oct. 2020

Fingerprint biometric is popularly used for protecting digital devices and applications. They are better and more reliable for authentication in comparison to the usual security tokens or password, which make them to be at the forefront of identity management systems. Though, they have several security benefits, there are several weaknesses of the fingerprint biometric recognition system. The greatest challenge of the fingerprint biometric system is theft or leakage of the template information. Also, each individual has limited and unique fingerprint which is permanent throughout their lifespan, hence, the compromise of the fingerprint biometric will cause a lifetime threat to the security and privacy of such an individual. Security and privacy risk of fingerprint biometric have previously been studied in the context of cryptosystem and cancelable biometric generation. However, these approaches do not obviously address the issue of revocability, diversity and irreversibility of fingerprint features to guard against the wrong use or theft of fingerprint biometric information.  In this paper, we proposed a model that harnesses the strength of Arnold transform and Bio-hashing on fingerprint biometric features to overcome the limitations commonly encountered in sole fingerprint biometric approaches. In the experimental analysis, the result of irreversibility showed 0% False Acceptance Rate (FAR), performance showed maximum of 0.2% FAR and maximum of 0.8% False Rejection Rate (FRR) at different threshold values. Also, the result of renewability/revocability at SMDKAB SMKADKB and SMKBDKA showed that the protection did not match each other. Therefore, the performance of the proposed model was notable and the techniques could be efficiently and reliably used to enforce protection on biometric templates in establishments/organizations so that their information and processes could be secured.

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