A Review on Biometric Template 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.

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.

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

A lightweight machine learning-based authentication framework for smart IoT devices

Biometric recognition is more and more employed in authentication and access control of various applications. Biometric data are strongly linked with the user and do not allow revocability nor diversity, without an adapted post-processing. Cancelable biometrics, including the very popular algorithm BioHashing, is used to cope with the underlying privacy and security issues. The principle is to transform a biometric template in a BioCode, in order to enhance user privacy and application security. These schemes are used for template protection of several biometric modalities, as fingerprints or face and the robustness is generally related to the hardness to recover the original biometric template by an impostor. In this paper, we propose to use genetic algorithms to approximate the original biometric feature and spoof the authentication system. We show through experimental results on fingerprints the efficiency of the proposed attack on the BioHashing algorithm, by approximating the original FingerCode, given the seed and the corresponding BioCode.

Cancelable biometrics has been proposed for canceling and re-issuing biometric templates and for protecting privacy in biometrics systems. Recently, new cancelable biometric approaches are proposed based on BioHashing, which are random transformed feature-based cancelable biometrics. In this paper, we consider the accuracy of one of the cancelable biometrics based on BioHashing and face. Through this analysis, as an illustration, we would like to raise an issue to be considered in cancelable biometrics: accuracy may be traded for biometrics being cancelable.

Deployments of biometric technologies are already widely disseminated, i.e. the protection of biometric reference data becomes vital in order to safeguard individuals' privacy. Biometric template protection techniques are designed to protect biometric templates in an irreversible and unlinkable manner (ISO/IEC IS 24745). In addition, these schemes are required to maintain key system properties, e.g. biometric performance or authentication speed. Recently, template protection schemes based on Bloom filters have been introduced and applied to various biometric characteristics, such as iris or face. While a Bloom filter-based representation of biometric templates is irreversible the originally proposed system has been exposed to be vulnerable to cross-matching attacks. In this paper we address this issue and demonstrate that any kind of Bloom filter-based representation of biometric templates can be transformed to an unordered set of integer values which enables a locking of irreversible templates in a fuzzy vault scheme from Dodis et al. which can be secured against known cross-matching attacks. In addition, experiments which are carried out on a publicly available iris database, show that the proposed scheme retains the biometric performance of the original system.

Invasion of user's privacy due to compromised biometric template draws great public concern recently. Cancelable biometric is proposed as a promising resolution by converting the original biometric template into a distorted version via a transformation function. However, certain schemes such as Densely Infinite-to-one mapping (DITOM) are merely applicable to fixed-length and ordered biometric features. This limits their application capacity on unordered and variable size features such as fingerprint minutia or descriptors. In this paper, we proposed an extended form of infinite-to-one transform that is capable to operate the two-dimensional feature vectors to generate a cancelable fingerprint template. The proposed method was evaluated using four public databases, i.e. FVC2002 DB1, DB2, DB3 and DB4. Experimental results demonstrate satisfactory accuracy performance. Besides, the changeability and non-invertibility are also justified.

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.

Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification databases. One methodology to achieve non-invertibility is the employment of non-invertible transforms. This work suggests an encryption process for cancellable speaker identification using a hybrid encryption system. This system includes the 3D Jigsaw transforms and Fractional Fourier Transform (FrFT). The proposed scheme is compared with the optical Double Random Phase Encoding (DRPE) encryption process. The evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure, authoritative, and feasible. The encryption and cancelability effects are good and reveal good performance. Also, it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

Cancelable biometric techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research presents an efficient cancelable biometric framework by exploiting an irreversible hybrid encryption technique that incorporates Deoxyribonucleic, Ribonucleic acid sequences (DNA and RNA), and the evolutionary optimization technique of the Genetic Algorithm (GA). So, these techniques are employed to create completely deformed templates from their original ones. Thus, our main contribution is offering a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA-based encryption scheme. The proposed framework produces an entirely unrecognized biometric template by ciphering the main discriminative features of the biometric traits of the authorized clients. The proposed algorithm is first initialized by creating several encrypted biometric images of the original users by employing the logistic map function. Secondly, the initialized encrypted images are transformed into one vector of a binary array. Then, they are converted to their corresponding introns, exons, and consequently, their relevant codons are stored in the cloud database. Thirdly, these relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key is extracted from the original biometric image after utilizing excessive permutations between its pixels. Then, the GA-based optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To validate the security efficacy of the proposed framework, different six biometric databases are used, which are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed framework is compared to those of recent studies in this field, such as OSH (Optical Scanning Holography) and DRPE (Double Random Phase Encoding). The simulation results prove the superior performance of the proposed framework in terms of all tested evaluation metrics. The proposed framework provides high AROC (Area under the Receiver Operating Characteristic) that reaches 0.9990, low FAR (False Acceptance Rate) of 0.0015, more uniform histograms, high correlation values for genuine users, and complete hidden of biometric features. In addition, from a security perspective, the proposed framework accomplishes good entropy, UACI (unified average changing intensity), and NPCR (number of pixels change rate) values that reach 7.9960, 33.55%, and 99.65%, respectively.

The main aim of biometric-based identification systems is to automatically recognize individuals based on their physiological and/or behavioural characteristics such as fingerprint, face, hand-geometry, among others. These systems offer several advantages over traditional forms of identity protection. However, there are still some important aspects that need to be addressed in these systems. The main questions are concerned with the security of biometric authentication systems since it is important to ensure the integrity and public acceptance of these systems. In order to avoid the problems arising from compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new representation for enrolment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to increase the effectiveness of the proposed ensemble systems, three feature selection methods will be used to distribute the attributes among the individual classifiers of an ensemble. The main aim of this paper is to analyse the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

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 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

Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion