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Open AccessArticle

MoLaBSS: Server-Specific Add-On Biometric Security Layer Model to Enhance the Usage of Biometrics

1
Information Technology and Services, Perrigo Plc., Allegan, MI 49010, USA
2
Department of Neuroscience, Central Michigan University, Mt. Pleasant, MI 48859, USA
*
Author to whom correspondence should be addressed.
Information 2020, 11(6), 308; https://doi.org/10.3390/info11060308
Received: 18 April 2020 / Revised: 28 May 2020 / Accepted: 2 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Cyberspace Security, Privacy & Forensics)
With high-paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security. Organizations encourage their members to use biometrics, but they are hesitant to use them due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems. We propose a server-specific add-on biometric security layer model (MoLaBSS) to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show good potential of this framework to enhance users’ confidence level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.
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Keywords: biometrics; mobile applications; data security; privacy; cyber security biometrics; mobile applications; data security; privacy; cyber security
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MDPI and ACS Style

Singh, B.; Singh, N. MoLaBSS: Server-Specific Add-On Biometric Security Layer Model to Enhance the Usage of Biometrics. Information 2020, 11, 308.

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