Earticle

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

Biometric authentication systems have major security weaknesses: risk of biometric information leakage, unreliability of authentication modules, and non-transparency of biometric information management. This paper presents BDAS, a new biometric authentication system using blockchain, which provides a decentralized and distributed mechanism for processing biometric authentication and an auditable mechanism for managing biometric information. BDAS’s evaluation demonstrates that it provides reliable and secure authentication compared to existing methods while introducing negligible performance overhead in real-world scenarios.

On-demand ride services and the rideshare infrastructure primarily focus on the minimization of travel time and cost. However, the safety of riders is overlooked by service providers. For driver authentication, existing identity management methods typically check the driving license, which can be easily stolen, forged, or misused. Further, background checks are not performed at all; instead, social profiles and peer reviews are used to foster trust, thereby compromising the safety and security of riders. Moreover, the present mechanism seems ineffective in discontinuing a malicious driver from offering the services. In this paper, we present DriverAuth?a fully transparent and easy-to-use authentication scheme for drivers that is based on common behavioral biometric modalities, such as hand movements, swipes, and touch-strokes while the drivers interact with the dedicated smartphone-based application for accepting the booking. A preliminary study of behavioral biometric-based approaches offers a usable verification mechanism on smartphones that could be a potential solution to improve the safety of riders in the emerging on-demand ride and the rideshare infrastructure.

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.

With the rapid growth in the development of smart devices equipped with biometric sensors, client identification system using biometric traits are widely adopted across various applications. Among many biometric traits, fingerprint-based identification systems have been extensively studied and deployed. However, to adopt biometric identification systems in practical applications, two main obstacles in terms of efficiency and client privacy must be resolved simultaneously. That is, identification should be performed at an acceptable time, and only a client should have access to his/her biometric traits, which are not revocable if leaked. Until now, multiple studies have demonstrated successful protection of client biometric data; however, such systems lack efficiency that leads to excessive time utilization for identification. The most recently researched scheme shows efficiency improvements but reveals client biometric traits to other entities such as biometric database server. This violates client privacy. In this paper, we propose an efficient and privacy-preserving fingerprint identification scheme by using cloud systems. The proposed scheme extensively exploits the computation power of a cloud so that most of the laborious computations are performed by the cloud service provider. According to our experimental results on an Amazon EC2 cloud, the proposed scheme is faster than the existing schemes and guarantees client privacy by exploiting symmetric homomorphic encryption. Our security analysis shows that during identification, the client fingerprint data is not disclosed to the cloud service provider or fingerprint database server.

Biometric authentication systems may suffer from decreasing recognition performance due to varying environmental conditions and sample ageing, which cause intra-class variability. Adaptive biometric authentication systems have been proposed to address these deficiencies by dynamically changing their sampling and recognition processes in response to changes in the operating environment. This paper provides the complete discussion on the design of an adaptive biometric authentication system through a systematic review of the existing literature to date. The review shows that further investigation is required to target mobile and/or wearable devices as well as to consider a continuous authentication methodology. In addition to this, the evaluation of adaptive biometric authentications with large-scale datasets is required to validate the feasibility of the system and its scalability in real-world with a comprehensive study of evaluation metrics.

Biometrics as a tool for information security has been used in various applications. Feature-level fusion is widely used in the design of multi-biometric systems due to its advantages in increasing recognition accuracy and security. However, most existing multi-biometric systems that use feature-level fusion assign each biometric trait an equal proportion when combining features from multiple sources. For example, multi-biometric systems with two biometric traits commonly adopt a 50?50 feature proportion setting, which means that fused feature data contains half elements from each biometric modality. In this paper, we investigate the impact of feature proportion on the matching performance of multi-biometric systems. By using a fingerprint and face based multi-biometric system that applies feature-level fusion, we employ a random projection based transformation and a proportion weight factor. By adjusting this weight factor, we show that allocating unequal proportions to features from different biometric traits yields different matching performance. Our experimental results indicate that optimal performance, achieved with unequal feature proportions, could be better than the performance obtained with the commonly used 50?50 feature proportion. Therefore, the impact of feature proportion, which has been ignored by most existing work, should be taken into account and more study is required as to how to make feature proportion allocation benefit the performance of multi-biometric systems.

바이오메트릭 정보를 이용한 인증방식은 사용자의 신체정보를 이용하여 신원을 확인 하고 시스템의 접근을 허가한 다. 요즘 들어, 패스워드나 보안토큰을 단독으로 이용하는 방식보다는 하나이상의 고유한 신체적, 행동적 형질에 기반 하여 개인의 생체 정보인 지문, 홍채 얼굴, 정맥 등을 활용하는 방식이 점차 증가하고 있는 추세이다. 2013년 Althobati 등이 WSN(Wireless Sensor Networks) 환경에 적합한 바이오메트릭 정보를 이용한 사용한 사용자 인증 스킴을 제안하 였다. 그러나 그들이 제안 프로토콜은 데이터 무결성에 대한 위협과 바이패싱 게이트웨이 공격에 취약하여 상호인증을 달성할 수 없었다. 본 논문은 이전에 제안된 논문의 취약점을 개선하여 WSN 환경에 적합한 안전한 프로토콜을 제안하였다.

Over recent years there has been considerable growth in interest in the use of biometric systems for personal authentication. Biometrics is a field of technology which has been and is being used in the identification of individuals based on some physical attribute. By using biometrics, authentication is directly linked to the person, rather than their token or password. Biometric authentication is a type of system that relies on the unique biological characteristics of individuals to verify identity for secure access to electronic systems. In 2013, Althobati et al. proposed an efficient remote user authentication protocol using biometric information. However, we uncovered Althobati et al.’s protocol does not guarantee its main security goal of mutual authentication. We showed this by mounting threat of data integrity and bypassing the gateway node attack on Althobati et al.’s protocol. In this paper, we propose an improved scheme to overcome these security weaknesses by storing secret data in device. In addition, our proposed scheme should provide not only security, but also efficiency since sensors in WSN(Wireless Sensor Networks) operate with resource constraints such as limited power, computation, and storage space.

Security is a primary concern in group communication, and secure authentication is essential to establishing a secure group communication. Most conventional authentications consist of knowledge-based and token-based methods. One of the token-based methods is a X.509 certificate, which is used under a Public Key Infrastructure (PKI); it is the most well-known authentication system in a distributed network environment. However, it has a well-known weakness, which only proves the belonging of a certificate. PKI cannot assure identity of a person. The conventional knowledge-based and token-based methods do not really provide positive personal identification because they rely on surrogate representations of the person’s identity. Therefore, I propose a secure X.509 certificate with biometric information to assure the identity of the person who uses the X.509 certificate in a distributed computing environment.

이 연구는 교도소에서 바이오메트릭 기술의 이용의 기본을 다룬다. 이를 위해 먼저 출입통제의 역사와 바이오메트릭 기술을 소개한다. 그리고 현재의 바이오메트릭 기술과 이것이 교도소의 작동을 어떻게 변화시키는지를 다룬다. 바이오메트릭 시스템을 현장에서 사용하는 것은 이 연구의 제 삼장에서 다룬다. 미래의 바이오메트릭 기술 및 기술의 발달과 함께 앞으로 교도소가 어떻게 변할지를 살펴보며 마무리 짓는다.

This paper addresses the basics of the use of biometrics in prison. It begins with a history of access control and the introduction of biometrics. It then addresses the current state of biometrics and how it is changing the way prisons are operated. Putting biometric systems into operation is the third section of the paper. It concludes with a look of the future of biometrics and how prisons maybe further changed with advancements in technology. The technological side of biometrics in prison has largely been proven successful. If there has been a failure in biometrics, it has typically come in the planning, testing, and installation of these systems. To ensure proper operation, and more importantly to ensure staff acceptance, proper planning and testing must go into implementing a biometric system. If a biometric system is properly designed, tested, and put into operation (along with proper training of staff), it can be expected to have a strong and positive influence on the efficiency and effectiveness of prison operations. The immediate future of security and access control in prisons appears to be inextricably linked with biometrics. Biometric tags are getting smaller, able to hold more information, and better able to be active rather than passive systems. This may mean that biometric systems may be placed in the body rather than on wristbands, further reducing the ability to tamper with the tags. Increases in facial and thumbprint recognition will likely increase the speed at which biometric systems will be able to process people at access point. We are very near a point where the whereabouts and access control of prisoners can be monitored and controlled 25 hours a day, seven days a week. Future advancements will strengthen that ability, and will add to the number of activities that can be made more efficient and more effective using biometrics.

The Metaverse, a rapidly evolving digital realm where people connect, collaborate, and engage in various activities, presents promising opportunities and, at the same time, raises significant security concerns. As users interact with diverse services, the need for a dependable method to confirm user identities becomes apparent. In light of current technology and the capabilities of biometrics, this paper explores the application of biometric authentication as a robust security solution within the Metaverse. Specifically, it investigates the practicality and effectiveness of biometric authentication methods, including facial recognition and iris scan, in preserving the integrity, confidentiality, and authenticity of interactions in the Metaverse. To illustrate our findings, we propose an authentication mechanism tailored for the Metaverse in response to the security challenges. To validate the feasibility and benefits of our approach, we conducted evaluations within an Apple Mac Mini environment, implementing biometric authentication in a simulated Apple Vision Pro. The outcomes of our study conclusively illustrate the security enhancements and advantages of our proposed authentication mechanism.

본 논문에서는 WMSN(무선 의료 센서 네트워크)을 위한 생체 인식 기반 인증 프로토콜의 보안 취약점을 분석하였다. 분석결과, 오프라인 비밀번호 추측, 위장 공격, 임시 비밀 유출 등의 취약점이 확인되었다. 이러한, 취약점을 개선하는 방법으로 솔트 암호화 해싱, 메시지 인증 코드, 디지털 서명, 개선된 키 합의 프로토콜 등 강화 된 전략을 제안하였다. 향후 리소스가 제한된 WMSN 환경에서 프로토콜의 견고성을 높이고 데이터 기밀성 및 무결성을 보장할 수 있는 고도화된 프로토콜의 개발이 요구된다.

This paper analyzes security vulnerabilities in a biometric-based authentication protocol for Wireless Medical Sensor Networks (WMSNs), identifying weaknesses like offline password guessing, impersonation, and ephemeral secret leakage. It proposes enhanced strategies, including salted password hashing, message authentication codes, digital signatures, and improved key agreement protocols, to bolster the protocol's robustness and ensure data confidentiality and integrity in resource-constrained WMSN environments.

The purpose of this study is to investigate the behavioral factors affecting the security attitude and intention to use biometrics password based on the protection motivation theory. This study also investigates security awareness training to understand trust, privacy, and security vulnerability regarding biometric authentication password. This empirical analysis reveals security awareness training boosts the protection motivational factors that affect on the behavior and intention of using biometric authentication passwords. This study also indicates that biometric authentication passwords can be used when the overall belief in a biometric system is present. After all, security awareness training enhances the belief of biometric passwords and increase the motivation to protect security threats. The study will provide insights into protecting security vulnerability with security awareness training.

The main purpose of this research is to understand the strongest predictors of wearable adoption among athletes with an emphasis on the perceived ethics of biometric data. We performed a word co-occurrence study of biometrics research to determine the ethical constructs of biometric data. A questionnaire incorporating the Unified Theory of Acceptance and Use of Technology (UTAUT), Health Belief Model and Biometric Data Ethics was then designed to develop a neural network model to predict the adoption of wearable sensors among athletes. Our model shows that wearable adoption’s strongest predictors are perceived ethics, perceived profit, and perceived threat; which can be categorized as professional stressors. The key theoretical contribution of this paper is to extend the literature on UTAUT by developing a predictive modeling of factors affecting acceptance of wearables by athletes, and highlighting the ethical implications of athlete’s adoption of wearables.

바이오인식은 각 개인의 신체적, 생리적, 행동적 특성을 자동화된 장치로 측정하여 이를 등록한 후, 개인을 식별하거나 인증하는 기술을 말한다. 그런데, 여기서 사용되는 바이오인식 정보는 개인을 식별할 수 있으므로 개인정보에 해당된다. 따라서, 이것이 유출되거나 오용되었을 때 정보주체의 프라이버시에 부정적인 영향을 미치게 된다. 본 논문에서는 바이오인식 정보와 관련된 국내의 법적 현황을 살펴보고 이와 관련된 침해현황을 살펴본다. 이어서 대표적인 바이오인식 응용 모델을 도출하고, 각각에 대한 취약점 및 대책 방안을 논의한다. 최종적으로 바이오인식 시스템의 개발자와 서비스제공자를 위해 바이오인식 정보의 보호를 위한 수칙을 제시한다.

Biometric recognition refers to a technology that identifies or verifies an individual after registering each individual's physical, physiological, and behavioral characteristics with an automated device. However, the biometric data used here corresponds to personal information since it can identify an individual. Therefore, when it is compromised or misused, it negatively affects the privacy of the data subject. In this paper, we review the current status of domestic laws related to biometric information and the status of infringements related to this. And then, some biometric application models are derived and vulnerabilities and countermeasures for each model are discussed. Finally, for the developer and service provider of the biometric system, protection guidance is presented.

본 논문에서는 프라이버시를 보호하기 위해 IoT(Internet of Things) 장치를 통해 수집한 생체 템플릿을 암호화하여 클라우드 환경에서 생체 인증을 수행하는 생체 인증 프레임워크 PPHDM-IoTC(Privacy-Preserving Hamming Distance Biometric Matching in IoT-Cloud architecture)를 제안한다. 제안한 PPHDM-IoTC을 RaspberryPI 4B 환경에서 C++로 구현하여 분석한 결과, Lightweight device 상에서도 효율적인 수행이 가능하며, 인증 단계에서 7.59~194.79µs (microseconds)가 소요된다는 것을 확인하였다. 덧붙여 클라우드 서버는 IoT 장치에서 전송받은 암호화된 생체 템플릿으로부터 민감 정보를 복구할 수 없으므로 프라이버시 침해 문제를 사전에 방지할 수 있다.

This paper proposes a biometric authentication framework, PPHDM-IoTC (Privacy-Preserving Hamming Distance Biometric Matching in IoT-Cloud architecture), which encrypts biometric templates collected through IoT(Internet of Things) devices to perform biometric authentication in a cloud environment, ensuring privacy protection. The proposed PPHDM-IoTC was implemented in C++ on a Raspberry Pi 4B environment, and the analysis showed that it can be efficiently executed even on lightweight devices, with authentication times ranging from 7.59 to 194.79 microseconds. Furthermore, the cloud server cannot recover sensitive information from the encrypted biometric templates sent by the IoT devices, thus preventing privacy breaches in advance.

IoT (Internet of Things)장치들은 열악한 환경, 저용량, 저성능 프로세서로 인해 기존의 유선망이나 무선망의 인증 방법을 적용하기가 어렵다. 특히 블록체인과 같은 방법을 IoT 환경에 적용하기에는 많은 문제점이 있다. 본 논문에서는 IoT 환경에서 생체정보 중 일회용 템플릿의 인증을 수행하는 서버 역할을 위해 엣지 컴퓨팅을 이용한다. 이와 같은 환경에서 ECC (elliptic curve cryptographic)를 기반으로 IoT-EC(edge computing) 시스템을 활용하여 가볍고 강한 인증 절차를 제안하고 이에 대한 안전성을 평가한다.

It is difficult to apply authentication methods of existing wired or wireless networks to Internet of Things (IoT) devices due to their poor environment, low capacity, and low-performance processor. In particular, there are many problems in applying methods such as blockchain to the IoT environment. In this paper, edge computing is used to serve as a server that authenticates disposable templates among biometric information in an IoT environment. In this environment, we propose a lightweight and strong authentication procedure using the IoT-edge computing (IoT-EC) system based on elliptic curve cryptographic (ECC) and evaluate its safety.

블록체인 기술은 분산된 신뢰구조를 제공하고, 위변조가 불가능한 시스템 구현이 가능하고, Smart Contract 등 이 가능하면서 인터넷 차세대 보안 기술로 발전하고 있다. 블록체인 기술이 차세대 보안기술로 부각되면서 무결성 뿐만 아니라 인증을 비롯하여 다양한 보안 서비스에 대한 연구가 진행되고 있다. 인터넷 기반의 다양한 서비스는 패스워드 기반의 사용 자 인증으로 이루어지고 있지만 클라이언트나 네트워크에서 가로채기가 가능하고, 패스워드 정보가 저장된 서버가 해킹의 위험에 노출되어 있다. 따라서 무결성을 보장할 수 있는 블록체인 기반 기술과 OTP 기반의 안전한 인증 기법을 제안한다. 특히, 이중 인증(Two-Factor Authentication) 은 OTP 기반의 인증과 사용자가 가지고 있는 생체인증의 결합으로 패스워드 없이 안전한 인증이 가능하다. 제안기법은 인증에 필요한 생체정보를 식별할 수 없도록 다중 해시함수를 적용하여 트랜잭션 을 생성하여 블록에 담기 때문에 서버로부터 분리되어 서버 공격에 안전하다.

Blockchain technology provides distributed trust structure; with this, we can implement a system that cannot be forged and make Smart Contract possible. With blockchain technology emerging as next generation security technology, there have been studies on authentication and security services that ensure integrity. Although Internet-based services have been going with user authentication with password, the information can be stolen through a client and a network and the server is exposed to hacking. For the reason, we suggest blockchain technology and OTP based authentication mechanism to ensure integrity. In particular, the Two-Factor Authentication is able to ensure secure authentication by combining OTP authentication and biometric authentication without using password. As the suggested authentication applies multiple hash functions and generates transactions to be placed in blocks in order for biometric information not to be identified, it is protected from server attacks by being separate from the server.

본 논문에서는 ATM(Automated Teller Machine: 금융자동화기기) 보안 강화를 위하여 적용할 수 있는 생체인식기술 중 지문인식, 홍채인식, 정맥인식 방식에 대하여 특징과 ATM 적용 사례 및 세부 사양 등을 분석하였다. ATM에서 생체인증을 도입할 경우 중요하게 고려해야 할 요건들을 기술하고, 이 요건들을 기준으로 각 생체인증 방식을 비교, 분석하였다. 분석 결과 인증정확도에 있어한계를 갖는 지문인식방식과 사용자 편의성에 있어 큰 약점이 있는 홍채인식방식 보다는, 인증정확도 및 은닉성이 우수하면서도위생적이라는 장점이 있는 정맥 인증 방식이 향후 ATM에 적용 확대 될 것으로 전망된다.

In this paper, we analyze the characteristics and ATM applications for fingerprint, iris, vein recognition technology that can be applied to the ATM to reinforce security. Describe the important requirements to be considered when introducing a biometric authentication in the ATM, and were compared to each biometric authentication scheme based on these requirements. Fingerprint authentication has limitations in accuracy, iris recognition is a big weakness in user convenience, whereas vein recognition has the advantage of being hygienic, yet excellent accuracy and secrecy. Vein authentication approach is expected to be expanded to apply to ATM due to the many advantages.