A robust minutia-based approach for securing fingerprint templates

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Biometric template protection is one of the important issues in deploying a practical biometric system. To tackle this problem, many algorithms have been reported in recent years, most of them being applicable to fingerprint biometric. Since the content and representation of fingerprint template is different from templates of other modalities such as face, the fingerprint template protection algorithms cannot be directly applied to face template. Moreover, we believe that no single template protection method is capable of satisfying the diversity, revocability, security and performance requirements. We propose a three-step cancelable framework which is a hybrid approach for face template protection. This hybrid algorithm is based on the random projection, class distribution preserving transform and hash function. Two publicly available face databases, namely FERET and CMU-PIE, are used for evaluating the template protection scheme. Experimental results show that the proposed method maintains good template discriminability, resulting in good recognition performance. A comparison with the recently developed random multispace quantization (RMQ) biohashing algorithm shows that our method outperforms the RMQ algorithm.

The need to protect biometric data has been well advised according to various regulations and standards. The most popular Bloom Filter-based template protection schemes for iris recognition directly depend on the keys to avoid linkability challenges. This work presents a new approach for generating the keys directly from the iris biometric data using chaotic maps and elliptic curves over finite fields. Further, we present a new template protection scheme that can directly exploit the generated keys to provide better security using a Quarter-Rounded template encoding which employs the inter-relation of bits in the neighborhood of the iriscode. The proposed approach is validated on the publicly available IITD Iris database and the comparison to the state-of-art Bloom Filter template protection. The proposed approach achieves a comparable performance with the Bloom Filter template protection while eliminating key generation overhead. The proposed template creation approach achieves Genuine Match Rate (GMR) = 99.15% at FMR= 0.01% and EER= 0.77% on the IITD Iris database, with an unlinkability score of 0 and superior revocability.

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

In recent years the security breaches and fraud transactions are increasing day by day. So there is a necessity for highly secure authentication technologies. The security of an authentication system can be strengthened by using Biometric system rather than the traditional method of authentication like Identity Cards (ID) and password which can be stolen easily. A biometric system works on biometric traits and fingerprint has the maximum share in market for providing biometric authentication as it is reliable, consistent and easy to capture. Although the biometric system is used to provide security to many applications but it is susceptible to different types of assaults too. Among all the modules of the biometric system which needs security, biometric template protection has received great consideration in the past years from the research community due to sensitivity of the biometric data stored in the form of template. A number of methods have been devised for providing template protection. Fuzzy vault is one of the cryptosystem based method of template security. The aim of fuzzy vault technique is to protect the precarious data with the biometric template in a way that only certified user can access the secret by providing valid biometric. In this paper, a modified version of fuzzy vault is presented to increase the level of security to the template and the secret key. The polynomial whose coefficients represent the key is transformed using an integral operator to hide the key where the key can no longer be derived if the polynomial is known to the attacker. The proposed fuzzy vault scheme also prevents the system from stolen key inversion attack. The results are achieved in terms of False Accept Rate (FAR), False Reject Rate (FRR), Genuine Acceptance Rate (GAR) by varying the degree of polynomial and number of biometric samples. It was calculated that for 40 users GAR was found to be 92%, 90%, 85% for degree of polynomial to be 3, 4 and 5 respectively. It was observed that increasing the degree of polynomial decreased the FAR rate, thus increasing the security

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Nowadays, biometric recognition represents an integral component of identity management and access control systems, replacing PINs or passwords. However, the wide deployment of biometric recognition systems in the past decades has raised numerous privacy concerns regarding the storage and use of biometric data. Due to the fact that the link between individuals and their biometric characteristics, e.g. fingerprints or iris, is strong and permanent, biometric reference data (templates) need to be protected in order to safeguard individuals' privacy and biometric systems' security. In particular, unprotected biometric templates can be abused to crossmatch biometric databases, i.e. tracking individuals without consent, and to launch presentation attacks employing specific inversion techniques. Technologies of biometric template protection offer solutions to privacy preserving biometric authentication, which improves the public confidence and acceptance of biometric systems. While biometric template protection has been an active research topic over the last 20 years, proposed solutions are still far from gaining practical acceptance. The existing gap between theory and practice, which results in a trade-off between privacy protection and recognition accuracy, is caused by numerous factors. To facilitate a standardized design and application of biometric template protection schemes, researchers have to solve several open problems. This chapter provides an overview of state-of-the-art technologies of biometric template protection and discusses main issues and challenges.

Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a token-less cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a feature-level fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the well-known Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER ~~ 0.1%.

Quantizing real-valued templates into binary strings is a fundamental step in biometric compression and template protection. In this paper, we introduce the area under the FRR curve optimize bit allocation (AUF-OBA) principle. Given the bit error probability, AUF-OBA assigns the numbers of quantization bits to every feature, in such way that the analytical area under the false rejection rate (FRR) curve for a Hamming distance classifier (HDC) is minimized. Experiments on the FRGC face database yield good performances.

In identity management system, commonly used biometric recognition system needs attention towards issue of biometric template protection as far as more reliable solution is concerned.In view of this biometric template protection algorithm should satisfy security, discriminability and cancelability.As no single template protection method is capable of satisfying the basic requirements, a novel technique for face template generation and protection is proposed.The novel approach is proposed to provide security and accuracy in new user enrollment as well as authentication process.This novel technique takes advantage of both the hybrid approach and the binary discriminant analysis algorithm.This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme.Three publicly available benchmark face databases are used for evaluation.The proposed novel technique enhances the discriminability and recognition accuracy by 80% in terms of matching score of the face images and provides high security.

Security analysis and improvement of some biometric protected templates based on Bloom filters

Online-to-Offline (O2O) e-commerce service platforms and their users are faced with various fraud risks. Among them, financial identity theft is a widely existing challenge. However, existing methods are insufficient to detect this type of fraud. In this paper, we address the financial identity theft detection problem in e-commerce services by leveraging access environment and behavior sequence. To explore the fraud patterns, we first make a detailed analysis using real cases of identity theft from Meituan, a leading O2O e-commerce platform in China. Our findings are twofold. First, fraudulent accounts sharing the same personal ID would have different access environments, such as devices and IP addresses. Second, a group of fraudulent accounts may have aggregations of devices, IP addresses, and delivery addresses. Based on these observations, we propose a hybrid method termed EnvIT to detect financial identity theft based on the heterogeneous graph and the behavior sequence. EnvIT is able to characterize the access environment and the historical behavior of the accounts. Furthermore, an attentive module is adopted to assign weights to different features automatically. We further evaluate EnvIT via extensive experiments using a real-world dataset from Meituan. Our experimental results demonstrate that EnvIt outperforms several baseline methods in fraudulent account detection and achieves an AUC of 0.9210.

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

The fuzzy vault is an innovative way to share secret keys, combining traditional cryptography with biometrics and biometric template protection. This method forms the basis for the reliable operation of unmanned aerial vehicles (UAVs) through anonymizing drone operators and safely using their data and onboard information. However, due to the inherent instability of biometrics, traditional fuzzy vault schemes face challenges, such as reduced recognition rates with increased chaff points, impractical runtimes due to high-order polynomial reconstruction, and susceptibility to correlation attacks. This paper proposes an efficient fuzzy vault scheme to address these challenges. We generate two secret keys based on biometrics: the first key is produced from the operator’s unique features like the face and iris, using a confidence interval; the second key, used to construct a polynomial, is based on what the operator remembers. These dual-key fuzzy vaults enable the stable generation of genuine points during encoding, easy extraction during decoding, and effective operator authentication while maintaining anonymity. Our experimental results demonstrate improved security and secret acquisition accuracy using the AR face database. These results are achieved regardless of increased false vaults, enabling real-time polynomial reconstruction and resilience against correlation attacks. Importantly, our enhanced fuzzy vault scheme allows the application of this secure, real-time authentication process, safeguarding the anonymity of drone operators.