Stolen Token Scenario Research Articles

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity With Noise (LPN) Commitments

Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), cross-matching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition system implemented in Matlab.

A Cryptanalysis of Two Cancelable Biometric Schemes Based on Index-of-Max Hashing

Cancelable biometric schemes generate secure biometric templates by combining user specific tokens and biometric data. The main objective is to create irreversible, unlinkable, and revocable templates, with high accuracy of comparison. In this paper, we cryptanalyze two recent cancelable biometric schemes based on a particular locality sensitive hashing function, index-of-max (IoM): Gaussian Random Projection-IoM (GRP-IoM) and Uniformly Random Permutation-IoM (URP-IoM). As originally proposed, these schemes were claimed to be resistant against reversibility, authentication, and linkability attacks under the stolen token scenario. We propose several attacks against GRP-IoM and URP-IoM, and argue that both schemes are severely vulnerable against authentication and linkability attacks. We also propose better, but not yet practical, reversibility attacks against GRP-IoM. The correctness and practical impact of our attacks are verified over the same dataset provided by the authors of these two schemes.

Random Slope method for generation of cancelable biometric features

Cancelable biometric templates are transformed versions of original biometric templates used for authentication purposes. The transformation functions should fulfill the important template protection criteria of diversity, revocability, non-invertibility, and performance. Although there exists a number of transformation techniques, yet many of these techniques fail to meet security and privacy requirements in the stolen token scenario and tend to become invertible with degraded performance. The work proposes a novel cancelable biometric technique named as ‘Random Slope’ method for generating secure, revocable, and non-invertible templates. Two approaches (RS-V1 and RS-V2) developed using the proposed concept not only fulfill the important cancelability criteria, but also provide dimensionality reduction upto 75%. The performances of the proposed approaches are experimentally verified for various biometric modalities such as visible and thermal face, palmprint, palmvein, and fingervein. As compared to some state of the art template transformation schemes, the proposed RS-V1 and RS-V2 approaches establish their reliability and effectiveness by performing better than these existing techniques with significant reduction in dimensions.

Design of a cancelable biometric template protection scheme for fingerprints based on cryptographic hash functions

Enforcing security and privacy guarantees for biometric users via protecting their unique and personalized attributes is an important area of research. There are many properties desirable in a biometric template protection scheme, but unfortunately, all of the requirements are not simultaneously fulfilled. In our work, we have tried to address this issue by proposing a cancelable framework for fingerprints which simultaneously provides satisfactory system performance, strong security guarantees, and fast matching procedure. The core of our scheme essentially pivots around the use of cryptographic hash functions which provide the adequate levels of security in the framework. Prior to the hashing stage, we have employed an effective pre-alignment technique and a hexagonal grid based quantization scheme which allows us to overcome constraint such as intra-class variability. Finally, our scheme is made cancelable through traditional salting. We tested our framework on various fingerprint databases and found that the resulting system performances were comparable with other contemporary cancelable schemes (EERs of 5.8%, 5.3%, 15.8%, 14.5% were observed for FVC2002 DB1, DB2, FVC2004 DB1, DB2 databases under the stolen token scenario). Most importantly, we perfectly fulfill the unlinkability, cancelability and diversity requirements, which are verified both theoretically and empirically.

Cancelable features using log-Gabor filters for biometric authentication

Wide spread use of biometric based authentication requires security of biometric data against identity thefts. Cancelable biometrics is a recent approach to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. This work proposes a template protection approach which generates revocable binary features from phase and magnitude patterns of log-Gabor filters. Multi-level transformations are applied at signal and feature level to distort the biometric data using user specific tokenized variables which are observed to provide better performance and security against information leakage under correlation attacks. A thorough analysis is performed to study the performance, non-invertibility, and changeability of the proposed approach under stolen token scenario on multiple biometric modalities. It is revealed that generated templates are non-invertible, easy to revoke, and also deliver good performance.

PalmHash Code vs. PalmPhasor Code

The existing palmprint protection schemes without verification degrading do not strictly meet non-invertible requirement; while non-invertible palmprint protection schemes do not meet verification performance requirement in stolen-token scenario. PalmHash Code and PalmPhasor Code, as two cancelable palmprint coding schemes, are proposed in this paper to balance the conflict between security and verification performance. In order to reduce computational complexity and storage cost, cancelable palmprint coding frameworks are extended from one dimension to two dimensions. Besides, two measures, perpendicular orientation transposition and multi-orientation score level fusion, are employed to further improve the performance of two dimensional (2D) cancelable palmprint codes. PalmHash Code and PalmPhasor Code are compared extensively in this paper. The experimental results and analysis confirm that 2D PalmHash Code and 2D PalmPhasor Code enhanced by the two measures are effective and secure even in stolen-token scenario.

Service-oriented architecture based on biometric using random features and incremental neural networks

We propose a service-oriented architecture based on biometric system where training and classification tasks are used by millions of users via internet connection. Such a large-scale biometric system needs to consider template protection, accuracy and efficiency issues. This is a challenging problem since there are tradeoffs among these three issues. In order to simultaneously handle these issues, we extract both global and local features via controlling the sparsity of random bases without training. Subsequently, the extracted features are fused with a sequential classifier. In the proposed system, the random basis features are not stored for security reason. The non-training based on feature extraction followed by a sequential learning contributes to computational efficiency. The overall accuracy is consequently improved via an ensemble of classifiers. We evaluate the performance of the proposed system using equal error rate under a stolen-token scenario. Our experimental results show that the proposed method is robust over severe local deformation with efficient computation for simultaneous transactions. Although we focus on face biometrics in this paper, the proposed method is generic and can be applied to other biometric traits.

A secure biometric discretization scheme for face template protection

In this paper, a dynamic biometric discretization scheme based on Linnartz and Tuyls’s quantization index modulation scheme (LT-QIM) [Linnartz and Tuyls, 2003] is proposed. LT-QIM extracts one bit per feature element and takes care of the intra-class variation of the biometric features. Nevertheless, LT-QIM does not consider statistical distinctiveness between users, and thus lacks the capability of preserving the discriminative power of the original biometric features. We put forward a generalized LT-QIM scheme in such a way that it allocates multiple bits to each feature element according to a statistical distinctiveness measure of the feature. Hence, more bits are assigned to high distinctive features and fewer bits to low distinctive features. With provision for intra-class variation compensation and dynamic bit allocation by means of the statistical distinctiveness measure, the generalized scheme enhances the verification performance compared to the original scheme. Several comparative studies are conducted on two popular face data sets to justify the efficiency and feasibility of our proposed scheme. The security aspect is also considered by including a stolen-token scenario.

A Novel Two-Stage Approach in Rectifying BioHash's Problem under Stolen Token Scenario

Over recent years, much research attention has been devoted to a two-factor authentication mechanism which integrates both tokenized pseudorandom numbers with user specific biometric features for biometric verification, known as Biohash. The main advantage of Biohash over sole biometrics is that Biohash is able to achieve a zero equal error rate and provide a clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without imperiling the false reject rates. Nonetheless, when the token of a user is compromised, the recognition performance of a biometric system drops drastically. As such, a few solutions have been proposed to improve the degraded performance but such improvements appear to be insignificant. In this paper, we investigate and pinpoint the basis of such deterioration. Subsequently, we propose a two-level approach by utilizing strong inner products and fuzzy logic weighting strategies accordingly to increase the original performance of Biohash under this scenario.

BioEncoding: A Reliable Tokenless Cancelable Biometrics Scheme for Protecting IrisCodes

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.

Cancellable biometrics and user-dependent multi-state discretization in BioHash

Although the use of biometrics for security access is convenient and easy to be implemented, it also introduced privacy and other security concerns when the original biometric templates are compromised. BioHash was introduced as a form of cancellable or replaceable biometrics through the integration of a set of user-specific random numbers with biometric features to address these concerns. However, the main drawback of the original form of BioHash is its inferior performance when an imposter obtains a legitimate token and uses it to claim as a genuine user (also known as the stolen-token scenario). In this paper, the problem is circumvented by a user-dependent multi-state discretization method. The experimental results on fingerprint database FVC2002 demonstrated a promising performance improvement on the stolen-token scenario when this discretization method was incorporated in the BioHash scheme. Moreover, the discretization method can render a long bit string, which is a useful feature to resist brute-force attacks. Some desired properties such as one-way transformation and diversity are also analyzed.