Generating Cancelable Fingerprint Templates

Abstract

Biometrics-based authentication systems offer obvious usability advantages over traditional password and token-based authentication schemes. However, biometrics raises several privacy concerns. A biometric is permanently associated with a user and cannot be changed. Hence, if a biometric identifier is compromised, it is lost forever and possibly for every application where the biometric is used. Moreover, if the same biometric is used in multiple applications, a user can potentially be tracked from one application to the next by cross-matching biometric databases. In this paper, we demonstrate several methods to generate multiple cancelable identifiers from fingerprint images to overcome these problems. In essence, a user can be given as many biometric identifiers as needed by issuing a new transformation "key." The identifiers can be cancelled and replaced when compromised. We empirically compare the performance of several algorithms such as Cartesian, polar, and surface folding transformations of the minutiae positions. It is demonstrated through multiple experiments that we can achieve revocability and prevent cross-matching of biometric databases. It is also shown that the transforms are noninvertible by demonstrating that it is computationally as hard to recover the original biometric identifier from a transformed version as by randomly guessing. Based on these empirical results and a theoretical analysis we conclude that feature-level cancelable biometric construction is practicable in large biometric deployments.

Towards security goals in summative e-assessment security

“…Another method is the use of biometrics, which is suggested as an ultimate identity and authentication technique for e-learning [11]. Irrespective of known biometric challenges [2,10,17], researchers explore the possibilities of employing biometrics in e-assessment. Current researches focus on multi-biometrics to support services for identity and authentication [3,16].…”

Unitary Transform-Based Template Protection and Its Application to <i>l</i>²-norm Minimization Problems

“…The template protection schemes proposed in literatures can be broadly classified into two categories, feature transformation approach and biometric cryptosystem [2], [3]. The former [4]- [16] has a high degree of freedom for signal processing in the protected domain, compared to the latter [17]- [21]. Feature transform schemes can be further categorized as salting biometric [4]- [8] and noninvertible [9]- [16] transforms.…”

Dictionary Attack on Functional Transform-Based Cancelable Fingerprint Templates

“…One of the most promising solutions to this problem is cancelable biometrics [2], where a system does not store the original biometric data; rather, it stores only the version transformed by a noninvertible transform [3]. Then, verification is done on this transformed data without any need to recover the original data, keeping the original data safe even if the system is compromised.…”