Proving the security of cancelable biometrics and other template protection techniques is a key prerequisite for the widespread deployment of biometric technologies. BioEncoding is a cancelable biometrics scheme that has been proposed recently to protect biometric templates represented as binary strings like iris codes. Unlike other template protection schemes, BioEncoding does not require user-specific keys or tokens. Moreover, it satisfies the requirements of untraceable biometrics without sacrificing the matching accuracy. However, the security of BioEncoding against smart attacks, such as correlation and optimization-based attacks, has to be proved before recommending it for practical deployment. In this paper, the security of BioEncopding, in terms of both non-invertibility and privacy protection, is analyzed. First, resistance of protected templates generated using BioEncoding against brute-force search attacks is revisited rigorously. Then, vulnerabilities of BioEncoding with respect to correlation attacks and optimization based attacks are identified and explained. Furthermore, an important modification to the BioEncoding algorithm is proposed to enhance its security against correlation attacks. The effect of integrating this modification into BioEncoding is validated and its impact on the matching accuracy is investigated empirically using CASIA-IrisV3-Interval dataset. Experimental results confirm the efficacy of the proposed modification and show that it has no negative impact on the matching accuracy.

Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. In this paper, we propose a novel and fundamental algorithm for cancelable biometrics called correlation-invariant random filtering (CIRF) with provable security. Then we construct a method for generating cancelable fingerprint templates based on the chip matching algorithm and the CIRF. Experimental evaluation shows that our method has almost the same accuracy as the conventional fingerprint verification based on the chip matching algorithm.

Despite their usability advantages over traditional authentication systems, biometrics-based authentication systems suffer from inherent privacy violation and non-revocability issues. In order to address these issues, the concept of cancelable biometrics was introduced as a means of generating multiple, revocable, and noninvertible identities from true biometric templates. Apart from BioHashing, which is a two-factor cancelable biometrics technique based on mixing a set of tokenized user-specific random numbers with biometric features, cancelable biometrics techniques usually cannot preserve the recognition accuracy achieved using the unprotected biometric systems. However, as the employed token can be lost, shared, or stolen, BioHashing suffers from the same issues associated with token-based authentication systems. In this paper, a reliable tokenless cancelable biometrics scheme, referred to as BioEncoding, for protecting IrisCodes is presented. Unlike BioHashing, BioEncoding can be used as a one-factor authentication scheme that relies only on sole IrisCodes. A unique noninvertible compact bit-string, referred to as BioCode, is randomly derived from a true IrisCode. Rather than the true IrisCode, the derived BioCode can be used efficiently to verify the user identity without degrading the recognition accuracy obtained using original IrisCodes. Additionally, BioEncoding satisfies all the requirements of the cancelable biometrics construct. The performance of BioEncoding is compared with the performance of BioHashing in the stolen-token scenario and the experimental results show the superiority of the proposed method over BioHashing-based techniques.

Cancelable approaches for biometric person authentication have been studied to protect enrolled biometric data, and several algorithms have been proposed. One drawback of cancelable approaches is that the performance is inferior to that of non-cancelable approaches. In this paper, we propose a scheme to improve the performance of a cancelable approach for online signature verification. Our scheme generates two cancelable dataset from one raw dataset and uses them for verification. Preliminary experiments were performed using a distance-based online signature verification algorithm. The experimental results show that our proposed scheme is promising.