Secure Biometric Authentication Research Articles

Genetic algorithms and deep learning for unique facial landmark-based key generation

Generating secure secret keys remains essential in the realm of biometric authentication systems. Traditional methods have often suffered from inefficiency, insecurity, or the requirement for additional hardware. In this study, an innovative approach to secret key generation is proposed, leveraging deep learning, facial feature extraction, genetic algorithms, and linear feedback shift registers (LFSRs). These techniques are combined to create robust, unique keys based on users' facial features. A convolutional neural network (CNN) is employed for the extraction of facial features from user images and for the optimization of LFSR parameters using a genetic algorithm. Furthermore, another neural network is utilized to establish a connection between facial features and the LFSR-generated output, resulting in the secret key. Rigorous evaluation of the method is conducted across various facial datasets, with a comparison against existing approaches. The results demonstrate the effectiveness of the method, yielding secret key length of 92,706 characterized by an entropy value of 3.24, a low average correlation value of 0.1554, and a high level of security. This research represents a significant advancement in secure biometric authentication, addressing the limitations of conventional key generation methods.

Blockchain integrated data processing model for enabling security in Education 4.0

The COVID-19 pandemic necessitated a swift shift to online learning, affecting students differently. We investigated the experiences of 62 students with disabilities in this new educational landscape. Online learning tools raise concerns about privacy and security, making it crucial to explore students' perceptions in these areas. Our findings reveal that while students with learning disabilities appreciate online learning's flexibility, they need more guidance and support. Neurodiverse students with learning disabilities are particularly aware of the need for a secure online learning environment. These insights underscore the unique educational needs of students with disabilities in online education. In Personal Records, authenticating individuals, especially those with visual impairments, is critical. Our research combines education with cutting-edge technologies, like blockchain and machine learning, to enhance biometric authentication for visually impaired individuals. Proposed work focuses the Highly Secure Blockchain-Based Compressive Sensing (HSBCS) system, which uses blockchain for data integrity and machine learning for secure biometric authentication. Our research focuses on education and includes comprehensive testing and performance assessments. Results highlight the educational value of the HSBCS system for Students, as it significantly improves Personal Records data security and accessibility. In conclusion, our research offers an innovative, secure solution for biometric authentication in Personal Records, with a strong emphasis on education. It empowers Students to access their student information securely and independently, while enhancing education on data security and integrity. This study underscores the importance of integrating emerging technologies into Personal Records to provide better experiences for Students and address their unique educational needs.

A novel privacy-preserving biometric authentication scheme.

Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users' biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.

Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece

Homomorphic encryption is a powerful mechanism that allows sensitive data, such as biometric data, to be compared in a protected way, revealing only the comparison result when the private key is known. This is very useful for non-device-centric authentication architectures with clients that provide protected data and external servers that authenticate them. While many reported solutions do not follow standards and are not resistant to quantum computer attacks, this work proposes a secure biometric authentication scheme that applies homomorphic encryption based on the Classic McEliece public-key encryption algorithm, which is a round 4 candidate of the NIST post-quantum standardization process. The scheme applies specific steps to transform the features extracted from biometric samples. Its use is proposed in a non-device-centric biometric authentication architecture that ensures user privacy. Irreversibility, revocability and unlinkability are satisfied and the scheme is robust to stolen-device, False-Acceptance Rate (FAR) and similarity-based attacks as well as to honest-but-curious servers. In addition to the security achieved by the McEliece system, which remains stable over 40 years of attacks, the proposal allows for very reduced storage and communication overheads as well as low computational cost. A practical implementation of a non-device-centric facial authentication system is illustrated based on the generation and comparison of protected FaceNet embeddings. Experimental results with public databases show that the proposed scheme improves the accuracy and the False-Acceptance Rate of the unprotected scheme, maintaining the False-Rejection Rate, allows real-time execution in clients and servers for Classic McEliece security parameter sets of 128 and 256 bits (mceliece348864 and mceliece6688128, respectively), and reduces storage requirements in more than 90.5% compared to the most reduced-size homomorphic encryption-based schemes with post-quantum security reported in the literature.

Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques.

Image based Biometric Authentication for Blockchain Integrated VANETs

Vehicle in vehicular ad hoc (VANETs) communicates guides about their traffic status remotely for further developing traffic security and effectiveness. In any case, in the security message trade process, pernicious aggressor can deduce a client's character, occupation and other delicate data through direction following, and could send off an assault that can bring about accidents. Moreover, wellbeing message trade is generally founded on outdoors radio, and consequently different security assaults, for example, various security attacks, such as bogus information attack and impersonation attack, also sent off to VANETs . To determine the previously mentioned security and protection issues, we propose secure and Lightweight Face Biometric Authentication using deep learning algorithm convolutional neural network for Blockchain Integrated VANETs called VeChain. The proposed scheme is appropriate for resolving issues connected with security and protection since it joins the sealed VeChain based plans with the side of the road unit (RSU) based plans. In light of Public Key Cryptography, the proposed plot preloads the underlying public boundaries and keys of the framework in each RSU and the On-Border unit (OBU). Moreover, this scheme accomplish security and protection necessities as well as opposes normal security assaults and adulterated message transmission assault. At last, the presentation assessment shows that the proposed scheme is more effective computationally and communicational than the current plans in marking and confirming VANETs messages

A Personalized Acoustic Interface for Wearable Human–Machine Interaction

AbstractCommunication and interaction with machines are changing our ways of life. However, developing an acoustic interface that simultaneously features waterproofness, wearability, high fidelity, and high accuracy for human–machine interaction remains a grand challenge. Herein, a waterproof acoustic sensor (WAS) as a wearable translation interface to communicate with machines is reported. Owing to the sound‐response ability of internal microparticles, the WAS holds a significantly broad frequency response range of 0.1–20 kHz, covering almost the entire human audible range. The WAS is stable against human perspiration, shows omnidirectional response, and displays an excellent frequency detection resolution of 0.0001 kHz. With a collection of compelling features, the WAS can serve as a wearable acoustic human–machine interface and a high‐fidelity auditory platform for music recording. Moreover, the WAS‐based acoustic interface holds a remarkable 98% accuracy for speech recognition with the assistance of an artificial intelligence algorithm. Finally, the WAS‐based acoustic interface demonstrates speaker verification and identification for implementation in highly secure biometric authentication systems and wireless control of an intelligent car using speech recognition. Such a WAS‐based acoustic interface represents the advancement of high‐fidelity translation platforms for human–machine interactions toward practical applications, including the Internet of Things, assistive technology, and intelligent recognition systems.

A generic framework for deep incremental cancelable template generation

In a post-COVID-19 world, extensive study of deep learning-based biometric authentication techniques prompts the need to secure them. Further, the biometric data is assumed to be largely immutable; thus, if it is compromised, it is lost forever. Hence, reliable and secure biometric authentication is of utmost importance. In this paper, we address the security and privacy concerns of biometric templates generated via deep networks. We propose a cancelable biometric authentication approach. The framework consists of a lightweight Convolutional Neural Network (CNN) with a few-shot enrollment for generating biometric templates. Further, to enhance biometric templates’ discriminative power and to provide revocability, biometric templates are projected onto a random subspace (based on the user-specific key). Later projected biometric templates are mapped onto robust n-bit unique codes (using a KNN classifier) and protected via. SHA-3 hash digest. Moreover, a real-world biometric authentication system is always dynamic (users keep on changing). Thus we have also integrated phase-wise incremental learning within a deep learning-based cancelable biometric authentication framework. This is the first work in which deep cancelable templates are generated incrementally to the best of our knowledge. We analyze the proposed scheme for its performance and privacy preservation on three benchmarks constrained iris data-sets and over one unconstrained iris data-set along with one publicly available knuckle data-set. Furthermore, it has been demonstrated that the proposed cancelable incremental framework strictly follows the four fundamental properties of cancelability viz. non-invertibility, unlinkability, revocability, and usability.

Secure biometric authentication with de-duplication on distributed cloud storage.

Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes.

Multispectral hand features for secure biometric authentication systems

SummaryWith rapid growth of mobile‐based computing, reliable security and user authentication methods are necessary. This article proposes a proof‐of‐concept biometric authentication method utilizing hand images collected in different light spectra. An analysis of similarity between pattern of blood vessels extracted from near‐infrared images and thermal images and assessment of the correlation between individual biometric features contained in each image type is also performed. Results indicate a large potential of using thermal images in biometrics more extensively than now. Also proposed and evaluated are biometric recognition methods based on images of the hand acquired in visible light, near infrared, and using thermal infrared sensors. Two approaches were used to assess information content in images of each type: one based on texture descriptor and one employing convolutional neural networks. In an evaluation gathering data from 104 subjects, the former yielded the lowest equal error rate (EER) of , whereas the latter approach gave for thermal images. Finally, fusion of different‐spectra modalities increases accuracy and further reduces EER to . This is, to the authors' best knowledge, the first study exploring the concept of fusing different spectral representations of the human hand for the purpose of biometric recognition.

Significance of the Transition to Biometric Template Protection: Explore the Future

The IT security paradigm evolves from secret-based to biometric identity-based. Biometric identification has gradually become more popular in recent years for handheld devices. Privacy-preserving is a key concern when biometrics is used in authentication systems in the present world today. Nowadays, the declaration of biometric traits has been imposed not only by the government but also by many private entities. There are no proper mechanisms and assurance that biometric traits will be kept safe by such entities. The encryption of biometric traits to avoid privacy attacks is a giant problem. Hence, state-of-the-art safety and security technological solutions must be devised to prevent the loss and misuse of such biometric traits. In this paper, we have identified different cancelable biometrics methods with the possible attacks on the biometric traits and directions on possible countermeasures in order to design a secure and privacy-preserving biometric authentication system. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation and Index of max hashing. We tested and evaluated the proposed novel method on a standard dataset and achieved good results.

Secure Elgamal Based Authentication Scheme for Cloud Assisted IOT Based Wireless Body Area Network

Now-a-days ireless Body Area Network (WBAN) is considered to be new era technique in which patient’s health record are monitored remotely by using wearable sensors from anywhere in the world. In such high-level communication, there is need of security services are required to protect the data being used by healthcare professionals and patients from intruders or attackers. Therefore, many researchers are showing their keen interest for security enhancement of WBAN architecture for secure communication. In this dissertation work, different security and privacy techniques are reviewed and analysed WBAN/IoT challenges as well their limitations based on the latest standards and publications. This research also covers the state-of-art security measures and research in WBAN. This research presents an ElGamal cryptosystem and biometric information authentication scheme for WBAN/IOT applications. This work observed that most of the authentication protocols using hash function and ElGamal cryptosystem for cloud-based applications are affected by security attacks and are unable to hide the actual identities of the end users during login session. Therefore, this work has introduced a secure biometric ElGamal-based authentication as well as data sharing schemes. The result analysis shows that the proposed work is better with respect to existing work with respect to execution time and cost as well as security level.

Finger-Vein Quality Assessment Based on Deep Features From Grayscale and Binary Images

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessment, the proposed solutions are limited as they depend on human expertise and domain knowledge to extract handcrafted features for assessing quality. We have proposed, recently, the first deep neural network (DNN) framework for assessing finger-vein quality, that does not require manual labeling of high and low quality images, as is the case for state of the art methods, but infers such annotations automatically based on an objective indicator, the biometric verification decision. This framework has significantly outperformed the existing methods, whether the input image is in grayscale or is binary. Motivated by these performances, we propose, in this work, a representation learning of finger vein image quality, where a DNN takes as input conjointly the grayscale and binary versions of the input image to predict vein quality. Our model allows to learn the joint representation from grayscale and binary images, for quality assessment. The experimental results, obtained on a large public dataset, demonstrates that our proposed method accurately identifies high and low quality images, and outperforms other techniques in terms of equal error rate (EER) minimization, including our previous DNN models, based either on grayscale or binary input.

PassBio: Privacy-Preserving User-Centric Biometric Authentication

The proliferation of online biometric authentication has necessitated security requirements of biometric templates. The existing secure biometric authentication schemes feature a server-centric model, where a service provider maintains a biometric database and is fully responsible for the security of the templates. The end-users have to fully trust the server in storing, processing, and managing their private templates. As a result, the end-users’ templates could be compromised by outside attackers or even the service provider itself. In this paper, we propose a user-centric biometric authentication scheme (PassBio) that enables end-users to encrypt their own templates with our proposed light-weighted encryption scheme. During authentication, all the templates remain encrypted such that the server will never see them directly. However, the server is able to determine whether the distance of two encrypted templates is within a pre-defined threshold. Our security analysis shows that no critical information of the templates can be revealed under both passive and active attacks. PassBio follows a “compute-then-compare” computational model over encrypted data. More specifically, our proposed threshold predicate encryption (TPE) scheme can encrypt two vectors x and y in such a manner that the inner product of x and y can be evaluated and compared to a pre-defined threshold. TPE guarantees that only the comparison result is revealed and no key information about x and y can be learned. Furthermore, we show that TPE can be utilized as a flexible building block to evaluate different distance metrics, such as Hamming distance and Euclidean distance over encrypted data. Such a compute-then-compare computational model, enabled by TPE, can be widely applied in many interesting applications, such as searching over encrypted data while ensuring data security and privacy.

Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

This paper proposes a novel secure biometric authentication scheme that hides the biometric features and the distance between the enrolled and authenticated biometric features, and prevent impersonation. To confirm that the proposed scheme has such properties, we formally model secure biometric authentication schemes by generalizing the related and proposed schemes. As far as we know, the proposed scheme is the first one that has been proved to satisfy all the properties. In particular, the proposed scheme achieves security under the decisional Diffie-Hellman assumption.

Design of a Secure Biometric Authentication Framework Using PKI and FIDO in Fintech Environments

Design of a Secure Biometric Authentication Framework Using PKI and FIDO in Fintech Environments

An Improved and Secure Biometric Authentication Scheme for Telecare Medicine Information Systems Based on Elliptic Curve Cryptography.

Telecare medicine information system (TMIS) offers the patients convenient and expedite healthcare services remotely anywhere. Patient security and privacy has emerged as key issues during remote access because of underlying open architecture. An authentication scheme can verify patient's as well as TMIS server's legitimacy during remote healthcare services. To achieve security and privacy a number of authentication schemes have been proposed. Very recently Lu et al. (J. Med. Syst. 39(3):1-8, 2015) proposed a biometric based three factor authentication scheme for TMIS to confiscate the vulnerabilities of Arshad et al.'s (J. Med. Syst. 38(12):136, 2014) scheme. Further, they emphasized the robustness of their scheme against several attacks. However, in this paper we establish that Lu et al.'s scheme is vulnerable to numerous attacks including (1) Patient anonymity violation attack, (2) Patient impersonation attack, and (3) TMIS server impersonation attack. Furthermore, their scheme does not provide patient untraceability. We then, propose an improvement of Lu et al.'s scheme. We have analyzed the security of improved scheme using popular automated tool ProVerif. The proposed scheme while retaining the plusses of Lu et al.'s scheme is also robust against known attacks.

A Feature Level Extraction based on Iris Recognition for Secure Biometric Authentication

recognition is most precise and consistent biometric identification system accessible in the current situation. The demand for an accurate biometric system that provides dependable identification and verification of an individual has increased over the years. A biometric system that provides reliable and accurate identification of an individual is an iris recognition system. This reliability is provided by unique patterns of human iris which differs from person to person up to an extent of identical twins having different iris patterns. This paper has proposed the hybridization of Hough Circular Transform, Scale Invariant Feature Transform and Genetic Algorithm. The genetic algorithm is applied to optimize the features set as obtained by Scale invariant feature transform. KeywordsRecognition, Hough Transformation, SIFT, Genetic Algorithm.

A secure biometric authentication based on PEKS

SummaryBiometrics refers to metrics related to human characteristics and traits such as finger prints. How to use biometric to replace an encryption key or an identity certificate efficiently and securely is a hot topic in this big data era. In this field, secure biometric authentication is an important application, which refers to automated authentication based on their encrypted biological and behavioral traits. Searchable encryption is a powerful technology supporting retrieval for encrypted data with specific encrypted keyword index. It also has a profound meaning in the big data field. In this paper, we propose a generic transformation from searchable encryption to secure biometric authentication and construct a specific secure biometric authentication scheme based on public key encryption with keyword search (PEKS). The security of this authentication relies on the distinguishability of trapdoors and indexes in PEKS. In our scheme, the user does not need to claim the target user to authenticate in authentication message, and we change the pattern of authenticating by one bit. Compared with some existing authentication scheme, the proposed scheme is more efficient in the practical application. Furthermore, searchable encryption is an earlier cryptographic system that has relatively mature methods. Our transformation from searchable encryption to secure biometric authentication presents a new direction of constructing authentication scheme. Copyright © 2015 John Wiley & Sons, Ltd.

New packing method in somewhat homomorphic encryption and its applications

Somewhat homomorphic encryption is public key encryption supporting a limited number of additions and multiplications on encrypted data. This encryption gives a powerful tool in performing meaningful computations with protecting data confidentiality, whose property is suitable mainly in cloud computing. In this paper, we focus on the scheme proposed by Brakerski and Vaikuntanathan, and present two types of packed ciphertexts in order to improve performance and reduce size of the encrypted data. One type of our packed ciphertexts is based on the message encoding technique proposed by Lauter, Naehrig and Vaikuntanathan. While their technique empowers efficient secure computation of sums and products over the integers, our second type of packed ciphertexts enables efficient secure computation of more complex functionalities such as multiple inner products and multiple Hamming distances. We apply our packing method to construct several protocols for secure biometric authentication and secure pattern matching computations. Our implementation shows that our method gives faster performance than the state-of-the-art work in such applications. Copyright © 2015 John Wiley & Sons, Ltd.