A key generation algorithm for biometric cryptosystems based on TD-ERCS

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.

The release of the latest iPhone device by Apple, named the iPhone 5s which incorporates a fingerprint-based biometric scanner, was met with a lot of criticism from the security and privacy community. It was soon demonstrated that the biometric reader on this new iPhone is just as vulnerable to spoofing attacks as devised by researchers such as Matsumoto et al. 2002. It is an excepted fact that making use of biometrics for effective security during the identification and authentication process is not recommended Woodward and Orleans, 2004. People leave latent biometric prints of their fingerprints on everything they touch. Biometric technology is vexed with this problem - a biometric characteristic is not essentially covert, as people deposit their biometric characteristics in various ways in the environment they interact with. This paper proposes an approach to allow a person to use a smart device such as the iPhone 5s, for secure biometric authentication over a networked environment for secure electronic transactions. The paper illustrates that a smart device can be considered as a 'smart token', to address the security concerns associated with biometric technology.

<p>Biometrics associated with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Commonly used biometric features are facial features, fingerprints, voice, facial thermo grams, iris, posture/gait, palm print, hand geometry etc. Compared with other biometric characteristics iris is the most stable and hence the most reliable biometric characteristic over the period of a lifetime. This proposed work provides comparative study of various filters of Wavelet Transforms in terms of size and PSNR of images<em>.</em></p>

Continuous authentication is a promising method for protecting computer systems in the event of compromise of primary authentication factors, such as passwords or tokens. Systems employing continuous authentication that rely on biometrics may not be restricted to a single biometric characteristic; rather, they can simultaneously utilize multiple characteristics and subsequently arrive at a conclusive decision based on their collective analysis outcomes. One of the significant challenges researchers encounter when investigating effective fusion in decision-making is the lack of data. At present, data generation primarily involves the creation of feature vectors or attack simulation. This paper introduces a method for directly generating distances derived from a Siamese neural network, utilizing the probability density function of an existing distribution. Through statistical analysis, we successfully generated 5000 samples that correspond to the initial distribution, which were then employed to discover the threshold values at which FAR and FRR were less than 1%. The methods developed can be further applied to identify the most efficient strategies for integrating the results of continuous authentication in systems that incorporate multiple biometric characteristics.

Biometric characteristics allow identifying an individual based on who they are. This paper focuses on using the of a subject in electronic identity documents. The International Civil Aviation Organization (ICAO) has endorsed the adoption of the as the globally interoperable biometric characteristic. Soon afterward, the International Standard Organization (ISO) proposed the ISO/IEC 19794-5 standard in accordance with ICAO directives. In this paper, a new approach for the evaluation of three ISO/ICAO requirements (unnatural skin tone, across the and flash reflection on skin) is presented. The results achieved by the proposed methods have overcome almost all the works in the literature. For the shadows across the face requirement, the proposed method obtained a result 41% better than the best result in the literature. The proposed method is able to keep the rejection rate equal to zero even when it does not achieve the best equal error rate.

The aim of this work is to develop an authentication method using keyboard typing based on fuzzy logic. The paper considers a function that takes into account the minimum and maximum limits of biometric characteristics and the dependence of the stability of keyboard typing on its stability. As a result, a deviation factor is introduced to determine how much the biometric characteristic of the user after normalization will vary from the average value, which in its turn allows to determine the percentage of authenticity of the computer information system user. As a result, the probability of occurrence of errors of the first and second kind is reduced in comparison with the existing methods of authentication.

Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum‐rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance‐based face recognition algorithms has been done at each level.

Biometric cryptosystems provide an innovative solution for cryptographic key generation, encryption as well as biometric template protection. Besides high authentication accuracy, a good biometric cryptosystem is expected to protect biometric templates effectively, which requires that helper data does not reveal significant information about the templates. Previous works predominantly follow an appropriate entropy definition to measure the security of biometric cryptosystems. In this paper, we point out limitations of entropy-based security analysis and propose a new security analysis framework that combines information-theoretic approach with computational security. In addition, we construct a fingerprint-based multibiometric cryptosystem (MBC) using decision level fusion. Hash functions are employed in our construction to further protect each single biometric trait. The experimental results and security analysis demonstrate that the proposed MBC provides stronger security and better authentication accuracy compared with a cryptosystem based on single biometric.

Theft of a template is theft of identity. Fingerprint identification is used by forensic department in identifying criminals because of its uniqueness. Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits. Fingerprint identification is commonly employed in Forensic science to support criminal investigations and in biometric systems such as civilian and commercial identification devices. The best way to identify criminals is using fingerprint verification which cannot be replaced. So such fingerprint acquired from the criminal should be safely stored and protected from hackers for future references. This paper mainly deals with protecting the criminal's record (fingerprint template). Templates represent intrinsic information about a criminal. The criminal record (Fingerprint template) should be protected from creation of physical spoof and replacement by imposter's template to gain unauthorized access. Due to these reasons, fool-proof techniques are required for securely storing the criminal fingerprint records. Fuzzy vault is a cryptographic construct that is designed to work with biometric features represented as an unordered set like minutiae points. Template protection is done by transformation based approaches and biometric cryptosystems. During transformation template is stored in a transformed form so that the original biometric template cannot be easily recovered. Fingerprint from the criminal is first preprocessed and minutiae points are extracted. Preprocessing is very essential because the creation of fuzzy vault highly depends on the quality of the input fingerprint image. Then image is binarized followed by thinning and then region of interest (ROI) is calculated finally followed by removal of false minutiae points. The remaining useful minutiae points are used in fuzzy vault generation. A polynomial is evaluated from the secret key which is combined with minutiae points to produce a secure vault. Some dummy points called chaff points are also added to increase the security. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction and the number of chaff points. In the proposed system an even more secured fuzzy vault is generated with combined features of fingerprint and palm print to enhance the security of the template stored.

Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.

Biometric authentication is now widely adopted across both consumer and enterprise applications, offering an intuitive and seamless user experience. However, research in the field has historically focused more on improving recognition accuracy than on addressing fundamental security and privacy concerns. As biometric technologies become increasingly pervasive, their exposure to a broad range of threats, including template leakage, identity theft, and cross-matching, raises critical challenges. Existing protection schemes such as cancelable biometrics, biometric cryptosystems, and privacy-preserving protocols suffer from significant limitations: they are often vulnerable to inversion, prone to residual linkability across reissuances, or computationally unsuited for edge devices. To address these issues, this work introduces SHADOW (Secure Hidden Authenticating Digital Objects in the Wild), a framework for secure and privacy-preserving biometric authentication based on on-device generation of non-invertible and revocable representations. SHADOW achieves template-protection properties including unlinkability and renewability. It leverages analytically defined oscillatory functions to construct a structured authenticating object that binds a hidden certificate to the protected template and supports client-aware verification via nonce-driven masking. Trusted hardware can be used as optional hardening for endpoint secret protection; it is not required for SHADOW's template-protection guarantees, while client integrity assumes the confidentiality of the device-held secret. The proposed binding strategy achieves formal irreversibility and unlinkability by design, while supporting renewability and minimal computational overhead. Experiments conducted on public face, iris and voice benchmarks demonstrate that SHADOW achieves comparable recognition performance while ensuring robustness against major attack vectors, including inversion, bruteforce, and record multiplicity. The framework is compatible with modern embedding-based biometrics and exhibits properties that can facilitate deployment in decentralized and edge computing environments.

Concerns on widespread use of biometric authentication systems are primarily centered around template security, revocability, and privacy. The use of cryptographic primitives to bolster the authentication process can alleviate some of these concerns as shown by biometric cryptosystems. In this paper, we propose a provably secure and blind biometric authentication protocol, which addresses the concerns of user's privacy, template protection, and trust issues. The protocol is blind in the sense that it reveals only the identity, and no additional information about the user or the biometric to the authenticating server or vice-versa. As the protocol is based on asymmetric encryption of the biometric data, it captures the advantages of biometric authentication as well as the security of public key cryptography. The authentication protocol can run over public networks and provide nonrepudiable identity verification. The encryption also provides template protection, the ability to revoke enrolled templates, and alleviates the concerns on privacy in widespread use of biometrics. The proposed approach makes no restrictive assumptions on the biometric data and is hence applicable to multiple biometrics. Such a protocol has significant advantages over existing biometric cryptosystems, which use a biometric to secure a secret key, which in turn is used for authentication. We analyze the security of the protocol under various attack scenarios. Experimental results on four biometric datasets (face, iris, hand geometry, and fingerprint) show that carrying out the authentication in the encrypted domain does not affect the accuracy, while the encryption key acts as an additional layer of security.

BIOFUSE: A framework for multi-biometric fusion on biocryptosystem level

Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.