Secure biometric template generation for multi-factor authentication

PATTERN RECOGNITION

卷:48

Article

Khan, Salman H.¹  Akbar, M. Ali²  Shahzad, Farrukh²  Farooq, Mudassar²  Khan, Zeashan^3,4 

[1] Univ Western Australia, Sch Comp Sci & Software Engn, Crawley, WA 6009, Australia

[2] Inst Space Technol, Next Generat Intelligent Networks Res Ctr, Islamabad 44000, Pakistan

[3] Riphah Int Univ, Islamabad 44000, Pakistan

[4] Riphah Int Univ, Fac Engn & Appl Sci, Dept Elect Engn, Islamabad 44000, Pakistan

关键词: Two factor authentication;    Biometric template protection;    Feature transformation;    Dynamic signature verification;    Biohashing;    Random projections;    Distance matching;   

DOI  :  10.1016/j.patcog.2014.08.024

来源: Elsevier

【 摘 要 】

In the light of recent security incidents, leading to compromise of services using single factor authentication mechanisms, industry and academia researchers are actively investigating novel multi-factor authentication schemes. Moreover, exposure of unprotected authentication data is a high risk threat for organizations with online presence. The challenge is how to ensure security of multi-factor authentication data without deteriorating the performance of an identity verification system? To solve this problem, we present a novel framework that applies random projections to biometric data (inherence factor), using secure keys derived from passwords (knowledge factor), to generate inherently secure, efficient and revocable/renewable biometric templates for users' verification. We evaluate the security strength of the framework against possible attacks by adversaries. We also undertake a case study of deploying the proposed framework in a two-factor authentication setup that uses users' passwords and dynamic handwritten signatures. Our system preserves the important biometric information even when the user specific password is compromised - a highly desirable feature but not existent in the state-of-the-art transformation techniques. We have evaluated the performance of the framework on three publicly available signature datasets. The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results. (C) 2014 Elsevier Ltd. All rights reserved.

【 授权许可】

Free