SHADOW: Secure Hidden Authenticating Digital Objects in the Wild.

This paper investigates the identity theft and how to effectively use the biometric technology in order to prevent it from happening. Over the past few years, identity theft has become one of the most serious financial threats to corporations in the United States. Identity theft is when a thief steals sensitive information in order to engage in large financial transactions. When a thief successfully steals an identity, he or she may take out large amounts of loans and make purchases in the victim’s name. Identity theft allows thieves to pose as the victim so that all of the thief’s actions will be projected as actions from the victims. Thieves can damage corporations by compromising corporate credit cards as well as file documents to change the legal address of the victim’s company. Throughout the past decade, corporations have been using variations of biometric technology to prevent identity theft. This paper presents a new scheme for corporate identity theft prevention using biometric technology. Specifically, we develop a biometric based authentication system consisting of encryption and decryption processes. To show the practicality of our proposed scheme, an attacker-centric threat mode is created.

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.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

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

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

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

Today’s conventional verification system for accessing PC and handheld devices does not incorporate any post login verification. This type of system fails to address risk involved after the initial login session. With increasing personalization of computing devices, the risk extends from the erstwhile focus on malafide intervention to the issue of improper personalization of data generated out of cooperative or inadvertent intervention. Thus to ensure authenticity of the user during their entire active login period, a continuous verification is required. The current paper considers continuous unobtrusive biometric authentication technology as an approach to eliminate this problem. It also analyzes additional requirements for the unobtrusive and continuous nature of the verification system- namely unavailability of any specific modality at a given time, performance under fluctuating quality of measurable parameters and different fusion scenarios for modalities. Finally, it provides a brief framework of continuous multimodal biometric authentication using passive modalities - namely face identification, keystroke dynamics, mouse dynamics, and fingerprint which are suitable for unobtrusive continuous authentication.

Biometrics is a new technology that authenticates an individual's identity via his or her unique physical or behavioural characteristics, such as the iris, fingerprints, hand geometry, voice and signature. Although its application is becoming pervasive in the public and private sectors due to the potential benefits of the technology, its adoption by end-users is progressing slowly. This study investigates multiple aspects of the benefits and risks that consumers perceive in using biometric technology. A survey was conducted by contacting the actual customers of an American bank that has utilised fingerprint technology at its ATMs. The proposed model was tested with current users as well as potential adopters of the target technology. We found that enjoyment was the most salient perceived benefit for using fingerprint ATMs for both current users and potential adopters. Banks thus may highlight intrinsic values, such as the novelty of biometrics, to motivate the use of the technology. However, to promote potential users' adoption decisions, banks need to educate them about the security benefits of financial transactions under the technology. The result also showed that the current users were highly concerned about information privacy risk in using the fingerprint ATMs. Therefore, banks are advised to develop internal policies to protect personal biometric data from any identity theft or illegal uses to encourage continuous usage by the current users.

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.

Cloud computing has achieved maturity, and there is a heterogeneous group of providers and cloud-based services. However, significant attention remains focused on security concerns. In many cases, security and privacy issues are a significant barrier to user acceptance of cloud computing systems and the advantages these offer with respect to previous systems. Biometric technologies are becoming the key aspect of a wide range of secure identification and personal verification solutions, but in a cloud computing environment they present some problems related to the management of biometric data, due to privacy regulations and the need to trust cloud providers. To overcome those problems in this paper, we propose a cryptobiometric system applied to cloud computing in which no private biometric data are exposed.

In this chapter, a specific issue is addressed that concerns the protection of privacy vis-à-vis the efforts to combat identity theft and protect personal identifying information. There are, in particular, measures undertaken by legislators that involve penal sanctions and the introduction of new technological means for identity verification. Also, identity management schemes are introduced, which are utilized by service providers, mainly in the e-business sector, in order to support controlled access to resources. The solutions undertaken to protect identity are seen as measures enhancing privacy, which is endangered by identity theft. Personal information is largely available in the information society and its collection by identity fraudsters is also possible. Therefore, an effective protection of information protection should also include the protection of identity. The downside of the identity protection approach is that identity management actually presents risks to privacy, since the processing of personal data takes place in this context and it is argued that there are certain implications concerning the lawfulness of the processing. The use of electronic authentication through electronic cards or biometrics on passports and identity cards pose privacy issues, too. Subsequently, the legislation concerning identity theft and identity related crime is outlined. This is followed by specific analysis of privacy issues concerning identity management and identity verification methods, with particular reference to biometrics.

Access control and security within higher education institutions are of paramount importance in safeguarding sensitive information and resources. Conventional authentication methods, such as passwords and identification cards, have proven susceptible to security breaches and identity theft. To address these vulnerabilities, this paper presents a novel biometric authentication scheme tailored to the unique needs of higher education institutions. Deploying a trustworthy user authentication system became a key responsibility for both access control and securing user's private data with the rapid rise of electronic crimes and their connected difficulties. For both private and public use, human biometric features including voice, finger, iris scanning, face, signature, and other features offer a solid security level. This paper provides a comprehensive overview of the biometric authentication scheme, outlining its architecture, functionality, and security measures. We also present the results of a pilot implementation within a higher education institution, demonstrating improved access security and user satisfaction. Ethical considerations and privacy safeguards are discussed to ensure responsible biometric data handling. For a long time, numerous biometric authentication solutions have been considered. owing to the distinctiveness of human biometrics, which was important in thwarting imposters' attacks. Only a few of the key issues endangering system integrity and impeding effective service delivery include identity theft, spoofing, and the reliability of authentication systems in higher education institutions. From the experiment the total number of tests was 15, as the threshold was one attempt. While fingerprint authentication typically took 2.67 seconds, palm vein authentication often took 9.15 seconds. Therefore, the palm vein was slower than the fingerprint in terms of speed. The structure of the hand and the distance between the palm and the scanner were the determining elements in pal's slow authentication speed. The palm vein system has a 93.33% accuracy rate compared to the fingerprint system's 60% accuracy rate, making it the preferable model to use in a higher education setting. A biometric system's success or failure is influenced by a variety of variables and application domains. The purpose of this work is to discuss an appropriate biometric authentication model that may be used to improve the reliability of biometric systems in institutions of higher learning. The proposed biometric authentication scheme offers a forward-looking solution to the access integrity challenges faced by higher education institutions. By adopting this technology, institutions can bolster their security posture, protect sensitive data, and provide a more convenient and secure access experience for students, faculty, and staff.

One of the most promising applications of speaker recognition technology is to assist individuals with disabilities. With the rapid advancement of the Internet of Things (IoT), integrating speaker recognition into IoT devices is becoming increasingly important, particularly for secure access in healthcare settings. Biometric authentication using speech signals offers a compelling solution due to the distinctive and robust features of human voice. This paper proposes a cancelable speech encryption algorithm based on the fast Walsh–Hadamard transform (FWHT) and the RSA encryption algorithm, designed specifically for IoT network access control. The speech signal is first transformed and compressed using FWHT to reduce intelligibility in the transform domain. Security is enhanced through the combination of FWHT and the RSA encryption scheme. The effectiveness of the proposed method is evaluated using the TIMIT and Chinese Mandarin speech corpora, demonstrating promising performance. The approach achieves an equal error rate (EER) of 5.6184e-04 on the TIMIT dataset and 0.0400 on the Chinese Mandarin dataset, with an area under the ROC curve (AUC) of 99.99% for both. While these results demonstrate the potential effectiveness of the framework for securing networks through cancelable biometrics and fast processing time, the study does not include an evaluation of computational complexity and scalability in resource-constrained IoT environments. Future work will address these limitations to further assess the robustness and practical applicability of the proposed approach.

In the digital era, identity verification plays a crucial role in online services such as banking, healthcare, education, and government applications. Traditional identity management systems rely on centralized authorities that store sensitive user data, making them vulnerable to data breaches, identity theft, and unauthorized access. This paper proposes a Decentralized Identity Verification System (DID) based on blockchain technology that enables secure, transparent, and usercontrolled identity management. The proposed system utilizes blockchain networks and smart contracts to store encrypted identity records, ensuring immutability and privacy. Users maintain control over their identity credentials while institutions can verify authenticity without accessing sensitive personal data. The decentralized architecture eliminates dependency on centralized authorities and enhances trust, privacy, and security in digital identity verification.

Though e-exams have their advantages like accuracy and speed reduced human intervention, there are some flaws as well. These flaws are security threats, the same way as traditional exams are exposed to them such as cheating or unauthorized access. Furthermore, e-exams are also exposed to threats like impersonation and tampering or accessing exam related data and information on servers. As a result, there is a need to establish a strong security framework within the e-exam system that universities adopt, such that smooth and timely conduction of exams can take place. The present study deals with such security threats of e-exam systems and provides a solution by proposing a new security framework, to deal with these threats. Based on the primary data, a security framework of e-exam system was proposed at university levels. This security model aimed to mitigate the threats determined during the survey, along with the strategies and system requirements suggested by the survey respondents. The proposed framework provides security at two levels. Firstly, at the university level using biometric authentication and user login authentication. The second level was the cloud level, where four security methods (encryption, anti-X, security system access and SQL injection) were added to secure the data in the cloud (application server side). Further it is suggested in the model to use Infrastructure as a service (IaaS) for cloud computing because of its various advantages and security options which have been discussed in the model.