Biometrics Based Privacy-Preserving Authentication and Mobile Template Protection

Abstract EN

Smart mobile devices are playing a more and more important role in our daily life.

Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data.

However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template.

Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue.

To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm.

Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack.

Experimental results on public databases and security analysis show the validity of the proposed cancelable template.