Biometric Template Protection Research Articles

Implementation face based cancelable multi-biometric system

This paper suggests novel two proposed cancellable biometric realization techniques recognition and template protection. In this paper, the Homomorphic Key (HK) encoding algorithm is utilized for cancelable face system. In the first suggested scheme, the HK algorithm is applied on the face images. The resultant map is encrypted, in order to the second HK utilized in the HK is produced from the image. This approach can be used to develop a frequency domain procedure for making this system for biometric template protection. The second algorithm presents a new approach to detect the features of face images depending the Speeded Up Robust Features (SURF) and Optimum Global Thresholding (OTSU) method. This algorithm is relied on scale space analysis with the number of SURF feature points as the key parameters for classification. Simulation results using evaluation metrics False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Area under ROC (AROC) prove that the first proposed cancelable biometric technique with the second key are best with comparing the other keys. The obtained results clear that the second suggested technique has sucesseded in detection the features of the skin, eyes, nose, hair, ears images and the face positions.

A review on performance,security and various biometric template protection schemes for biometric authentication systems

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication systems. Security of biometric authentication needs to be reviewed and discussed as there are multiple points related to integrity and public reception of biometric-based authentication systems. Security and recognition accuracy are the two most important aspects which must be considered while designing biometric authentication systems. During enrollment phase scanning of biometric data is done to determine a set of distinct biometric feature set known as biometric template. Protection of biometric templates from various hacking efforts is a topic of vital importance as unlike passwords or tokens, compromised biometric templates cannot be reissued. Therefore, giving powerful protection techniques for biometric templates and still at that very moment preparing great identification accuracy is a good research problem nowadays, as well as in the future. Furthermore, efficiency under non-ideal conditions is also supposed to be inadequate and thus needs special attention in the design of a biometric authentication system. Disclosure of various biometric traits in miscellaneous applications creates a severe compromise on the privacy of the user. Biometric authentication can be utilized for remote user authentication. In this case, the biometric data of users typically called templates are stored in a server. The uniqueness and stability of biometrics ended it useful over traditional authentication systems. But, a similar thing made the enduring harm of a user’s identity in biometric systems. The architecture of the biometric system leads to several hazards that lead to numerous security concerns and privacy threats. To address this issue, biometric templates are secured using several schemes that are categorized as biometric cryptosystems, cancelable biometrics, hybrid methods, Homomorphic Encryption, visual cryptography based methods. Biometric cryptosystems and cancelable biometrics techniques provide reliable biometric security at a great level. However, there persist numerous concerns and encounters that are being faced during the deployment of these protection technologies. This paper reviews and analyses various biometric template protection methods. This review paper also reflects the limitations of various biometric template protection methods being used in present times and highlights the scope of future work.

Bio‐PUF‐MAC authenticated encryption for iris biometrics

AbstractBiometrics is one of the ways for human authentication. Fabrication of biometrics by intruders, limits the accuracy of authentication. The user‐specific keys (ie,) pseudo‐random numbers give more security for biometric template protection and increase the accuracy of authentication also. The user‐specific token or keys can also be fabricated by intruders by any of the prediction methods. To avoid the creation of fake biometric and fake user‐specific keys, a device‐specific Physical Unclonable Function (PUF) is proposed. In this article, iris authentication is provided by unclonable PUF‐based true random numbers to enhance the unique authentication. Nonreversible Message Authentication Codes (MAC) are developed using PUF and Discrete Wavelet Transform features of iris biometrics. Systematically, MAC codes also created with, encryption algorithm. Encryption is additionally providing confidentiality in the individual iris. Experiments are done with CUHK Iris Image Dataset. Proposed Bio‐PUF system has significant functional advantages in point of view of the unclonable pseudo‐random number from PUF. Experimentally, Avalanche effect, entropy, NCPR, and UACI parameters are analyzed with PUF‐based crypt functions. For 75% of matching with the Bio‐PUF‐MAC codes with enrolment, the accuracy for correct identification is 77.73%.

Comprehensive survey on cancelable biometrics with novel case study on finger dorsal template protection

In order to use biometric based authentication in banking domain, it is vital to secure biometric databases from unauthorized access. As we all know a biometric trait is immutable, thus if it is stolen it is lost forever. Therefore, securing biometric databases is a paramount concern in today’s digital World. In this paper, we have presented a comprehensive study of biometric template protection schemes majorly focusing on cancelable biometric techniques. Moreover, we have proposed a cancelable finger dorsal template generation network trained on trait specific features without using any pre-trained network framework. The highly discriminative features learned from our network are further hashed using BioHashing technique to generate cancelable finger dorsal templates. The experimental results are evaluated on two benchmark publicly available finger knuckle databases: PolyU FKP and PolyU Contactless FKI. We have also performed in depth security analysis of the proposed framework in terms of invertibility, revocability and unlinkability.

Open-set face identification with index-of-max hashing by learning

Large-scale face identification or 1-to-N matching where N is huge, plays a vital role in biometrics and surveillance. The system demands accurate and speedy matching where compact facial feature representation and a simple matcher are favored. On the other hand, most research considers closed-set identification that assumes that all identities of probe samples are enclosed in the gallery. On the contrary, open-set identification expects that some probe identities are not known to the system. This setup poses an additional challenge, where the system should be able to reject those probes that correspond to unknown identities. In this paper, we address the large-scale open-set face identification problem with a compact facial representation that is based on the index-of-maximum (IoM) hashing, which was designed for biometric template protection. To be specific, the existing random IoM hashing is advanced to a data-driven based hashing technique, where the hashed face code can be made compact and matching can be easily performed by the Hamming distance, which can offer highly efficient matching. Furthermore, since IoM hashing transforms the original facial features non-invertibly, the privacy of users can also be preserved. Along with IoM hashed face code, we explore several fusion strategies to address the open-set face identification problem. The comprehensive evaluations are carried out with three large-scale unconstrained face datasets, namely LFW, VGG2 and IJB-C.

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.

Cancelable Multi-Biometric Approach Using Fuzzy Extractor and Novel Bit-Wise Encryption

The widespread deployment of multi-biometrics to authenticate users prompts the need for biometric systems with high recognition performance. Further, the biometric data, once leaked or stolen, remains compromised forever. Hence biometric security is of utmost importance. Existing biometric template protection schemes either degrade the recognition performance or they have issues with security and speed. We propose a cancelable multi-biometric authentication approach where a novel bit-wise encryption scheme transforms a biometric template to a protected template using a secret key generated from another biometric template. It fully preserves the number of bit-errors in the original and the protected template to ensure recognition performance equivalent to the performance of the unprotected systems. We introduce Algorithm I and Algorithm II for bit-wise encryption; both are defined over cryptographic-primitives- block cipher based encryption and keyed-hash function. We profile these algorithms on various hardware architectures to calculate the efficiency in terms of the time taken during enrolment and authentication phase. For Algorithm II , we observe that a 3.3 GHz desktop architecture takes about 18 milliseconds on an average of over 200 runs to authenticate a user. Additionally, we provide mathematical proof to show that the proposed scheme guarantees secrecy and irreversibility. The results of comparisons with the existing biometric template protection schemes on the various face and iris databases show that the proposed work provides significantly good recognition performance and efficiency, while it achieves high security. Finally, the bit-wise encryption scheme can be built over the commercial-off-the-shelf systems to achieve security with equivalent high performance.

Evaluating biometrics fingerprint template protection for an emergency situation

Biometric template protection approaches have been developed to secure the biometric templates against image reconstruction on the stored templates. Two cancellable fingerprint template protection approaches namely minutiae-based bit-string cancellable fingerprint template and modified minutiae-based bit-string cancellable fingerprint template, are selected to be evaluated. Both approaches include the geometric information of the fingerprint into the extracted minutiae. Six modified fingerprint data sets are derived from the original fingerprint images in FVC2002DB1_B and FVC2002DB2_B by conducting the rotation and changing the quality of original fingerprint images according to the environment conditions during an emergency situation such as wet or dry fingers and disoriented angle of fingerprint images. The experimental results show that the modified minutiae-based bit-string cancellable fingerprint template performs well on all conditions during an emergency situation by achieving the matching accuracy between 83% and 100% on FVC2002DB1_B data set and between 99% and 100% on FVC2002DB2_B data set.

Accomplishment of New Protocol for Stupendous Security in Cloud Environment

Cloud is that the rising and thirst area of analysis and advantageous in all the fields. However security is the main disquiet for not espouse cloud for each application. Mainly of the security crisis are connected by authentication with data protection by the respect to cloud security alliance (CSA). The projected New (Biometric encoding and Biometric authentication) protocol can conquer every safety crisis in cloud adjacent. In NEW protocol biometric encryption has been provided for the cloud consumer’s valuable information and identity verification has been utilized in a unique way to scale back the problems associated with authentication and authorization. In NEW protocol Identity verification in cloud environment has been joint by pattern security in coincidence with four entirely dissimilar and influential encryption algorithms for accumulated safety. This protocol improves biometric template protection by the combination of RSA and AES encryption algorithms in proper locations and 3DES, Blowfish has been utilized in information security and solution safety supervision. By implementing such technique can vanish out the un trustiness of adopting cloud, specifically public and hybrid clouds. Since all the users information are hold on in off premise. Adopting this protocol has given nice results when examining with existing work and all the vulnerable places has been considered for improved security.

An Improved Secret Sharing Scheme for Biometric Authentication using Finite Field

Traditional biometric schemes used master copy minutiaefor authentication. The enrollment and verification process carried out through the same master copy minutiae. However, the protection of biometric template is more crucial as the number of minutiae compromise will be occurred. In this paper, an improved secret sharing scheme for biometric authentication using finite field is proposed. The master copy of biometric template is transformed into finite field for masking minutiae information. The concept of secret sharing is employed to split the template into multiple copies and maintained between smart card and public consortium. This approach allows perfect biometric authentication for operating smart cards with high-level security.

A fingerprint based crypto-biometric system for secure communication

To maintain secrecy of information during communication, cryptography is considered to be an impressive solution and cryptographic keys play an important role to ensure the security. However, these randomly derived keys (of 256 bits) are hard to memorize. Also, there is a threat of privacy invasion since the storage, protection and transmission of a key over a communication link may lead to information leakage. Therefore, researchers propose to utilize user’s biometric trait to generate the cryptographic key in a session-based communication environment. This avoids the storage of cryptographic keys without negotiating on secrecy. The biometric-based key generation encompasses concerns over biometric template protection, biometric data sharing between users and revocable key generation from biometric. To address the aforementioned concerns, we propose a framework for secure communication between two users using a fingerprint-based crypto-biometric system. First, the feature bit-string are computed from the users’ fingerprint. Next, revocable transformation is applied to derive the private keys of respective users. Then, the Diffie–Hellman (DH) algorithm is used to generate public keys from private keys of both sender and receiver, which are shared and further used to produce a symmetric cryptographic key at both ends. Here, the biometric data is neither stored nor shared which ensures the security of biometric data. Also, perfect forward secrecy is achieved using session keys. This work also provides the long-term protection of messages communicated between two users. It is evident from the experimental evaluation over four datasets of FVC2002, four datasets of FVC 2004, and NIST special database IV that the proposed framework is privacy-preserving and could be utilized for real access control systems.

GREYC-Hashing: Combining biometrics and secret for enhancing the security of protected templates

Abstract Template protection is a crucial issue in biometrics. Many algorithms have been proposed in the literature among secure computing approaches, crypto-biometric algorithm and feature transformation schemes. The BioHashing algorithm belongs to this last category and has very interesting properties. Among them, we can cite its genericity since it could be applied on any biometric modality, the possible cancelability of the generated BioCode and its efficiency when the secret is not stolen by an impostor. Its main drawback is its weakness face to a combined attack (false acceptance with the stolen secret scenario). In this paper, we propose a transformation-based biometric template protection scheme as an improvement of the BioHashing algorithm where the projection matrix is generated by combining the secret and the biometric data. Experimental results on three biometric modalities, namely digital fingerprint, finger knuckle print and hands vein images, show the benefits of the proposed method face to attacks while keeping a good efficiency.

Approach for Protection of Iris Template using Cancelable Biometrics

Background and Objective: The most important issue concerning the security of biometric authentication systems is protection of biometric templates. This is because once the biometric template being attacked, it cannot be canceled and reissued. Thus, the intruder could avail the facilities that are meant only for the genuine user just bypassing the enrollment phase. Methods: To protect the biometric templates from attacks, it is advantageous to modify them before storing in the databases through some cancelable and non-invertible transformations. Hence, an approach based on cancelable biometrics is proposed in this paper for providing security and privacy to biometric templates. It uses left iris and right iris as input biometric traits. Different experiments are carried out to authenticate the proposed approach. Results and Conclusion: It satisfies all template protection requirements and expected to show good recognition performance without degrading it.

Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Combining Watermarking and Hyper-Chaotic Map to Enhance the Security of Stored Biometric Templates

AbstractThere are a number of issues related to the development of biometric authentication systems, such as privacy breach, consequential security and biometric template storage. Thus, the current paper aims to address these issues through the hybrid approach of watermarking with biometric encryption. A multimodal biometric template protection approach with fusion at score level using fingerprint and face templates is proposed. The proposed approach includes two basic stages, enrollment stage and verification stage. During the enrollment stage, discrete wavelet transform (DWT) is applied on the face images to embed the fingerprint features into different directional sub-bands. Watermark embedding and extraction are done by quantizing the mean values of the wavelet coefficients. Subsequently, the inverse DWT is applied to obtain the watermarked image. Following this, a unique token is assigned for each genuine user and a hyper-chaotic map is used to produce a key stream in order to encrypt a watermarked image using block-cipher. The experimentation results indicate the efficiency of the proposed approach in term of achieving a reasonable error rate of 3.87%.

A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme

Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme.

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

The Internet of Things (IoT) is the next generation plethora of interconnected devices that includes sensors, actuators, etc. and that can provide personalized services such as healthcare, security, and surveillance. The quality of our daily lives is improved by the IoT through pervasive computation and communication. Innumerable devices are being connected each day to IoT applications. Although the quality of our lives is enhanced by the IoT, IoT applications also cause serious challenges in securing networks and data in transit. Existing security solutions, such as password-based two-factor authentication and traditional biometric template-based authentication, can be challenged because of several threats that affect the reliability and efficiency of the entire system. Hence, there is a need for a highly secure authentication mechanism such as the Cancelable Biometric System (CBS). In essence, the CBS is a biometric template protection scheme that operates based on repeated distortions/transformations at the feature/signal level. Therefore, in this paper, we propose a framework for a cloud-based lightweight cancelable biometric authentication system. Findings from our study are used to demonstrate the potential for the proposed approach to be deployed in real-world settings (i.e., the capability to authenticate client devices with high accuracy and minimal overhead without affecting the security of the sensitive biometric templates in the cloud environment). Both theoretical and experimental analyses suggest that the proposed approach has a minimal equal error rate compared with those of the state-of-the-art techniques. Moreover, the proposed approach has been proven to consume less time, making it suitable for IoT environments.

Cancelable fingerprint template design with randomized non-negative least squares

Although biometrics is considered more competent than password-based or token-based approach in identity management, biometric templates are vulnerable to adversary attacks that may lead to irreversible identity loss. One of the promising remedies for biometric template protection is cancelable biometrics. In this paper, a novel binary cancelable fingerprint template design based on Partial Local Structure (PLS) descriptor and Permutated Randomized Non-Negative Least Square (PR-NNLS) is proposed. The PLS descriptor is an alignment-free minutia descriptor, which is conceived to be coupled with the PR-NNLS to derive a binary protected fingerprint template that satisfies non-invertibility, unlinkability, cancelability and performance criteria. The PR-NNLS formulation is unique in such a way that the noninvertible transformation is applied to the PLS descriptor dictionary instead of applying it to the minutiae descriptor, which often invites performance deterioration. The evaluations have been carried out with five subsets from FVC 2002 and 2004 databases where the proposed method is attested to fulfill the aforementioned four template protection criteria. We also analyze four privacy and security attacks targeted to cancelable biometrics.

SCAN-speech biometric template protection based on genus-2 hyper elliptic curve

Rapid growth in the mobile technology manifolds the usage of mobile devices. It leads to the development and popularity of mobile application in diversified domains like finance, commerce, and government services. Extensive services can be explored conveniently and cost-effectively from these applications using speaker recognition based authentication to determine the identity of the individuals requesting service. Speech template that is stored in these mobile devices necessitates the prime concern of preserving the private data from unauthorized access and privacy breaches. It is highly feasible to prove the authenticity of the person using speech as a biometric to ensure that the rendered services are accessed only by the legitimate user. If the speech template stored in the database for comparison is compromised, then the authentication process will become obsolete. This issue had motivated the need for highly secured, faster and lightweight model named as (SCAN) - Speech biometriC templAte protectioN system based on the genus-2 hyper elliptic curve. Mobile devices are smaller in size and often restricted by memory and power constraints. Hence it requires a cryptosystem with lesser key size offering the higher degree of security and guard against sophisticated attacks. The proposed SCAN system provides the complete solution for this challenge uniquely by designing genus-2 hyper elliptic curve cryptosystem for speech template. The elaborate analysis from the results traces ideal values for MSE, PSNR, BRT, and EUD. This ratifies the suitability of this work that craves for higher encryption and decryption reliability. It offers high resistance to various attacks as it involves HECC that comes under the category of discrete logarithmic problem. This SCAN system can also reap the benefit of light weight processing, better ERR and authentication accuracy with induced parallelism.

Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT

With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.