Authentication Framework Research Articles

Delegated Authorization Framework for EHR Services Using Attribute-Based Encryption

Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients end. This creates a significant overhead for the patient who must authorize every access of their health record. This is not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization. Hence there is a need to develop a proper authorization delegation mechanism for safe, secure and easy to use cloud-based EHR Service management. We present a novel, centralized, attribute-based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHRs access authority to medical providers.

Blockchain Integrated Healthcare Wireless Body Area Network for Security Enhancement

A greater standard of network confidentiality and protection is generally accepted as a vital component of securing this data when employed by medical practitioners as well as during storage to guarantee that patient data are maintained safe from intruders. This paper develops block chain cryptography and bio-informatics verification architecture for WBANs implementations. As a result, there is a deep desire to solve security and privacy challenges with WBANs. WBANs confront a wide range of problems. Because of its versatility in a wide range of applications, WBAN is a favoured battleground for cybercriminals. This work also provides a blockchain integrity check and bio-informatics authentication framework for WBANs implementations. Hence there is an important interest to address security and privacy problems in WBANs.

Distributed authentication framework for Hadoop based bigdata environment

Distributed authentication framework for Hadoop based bigdata environment

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.

Woke brand activism authenticity or the lack of it

The increasing popularity of woke brand activism, tied to movements such as Black Lives Matter and Me Too, is creating new opportunities for brands to signal their responsibility and concerns toward social issues. However, such woke activism can lead to consumer backlash, hence carrying significant risks. Brands struggle to convince the public that their virtue-signaling efforts are authentic. Thus, this study explores the dimensions of woke branding authenticity, adopting an interpretive inductive research approach, and conducting content analysis. This study identifies six dimensions for woke activism authenticity: social context independency, inclusion, sacrifice, practice, fit, and motivation. By exploring different drivers of woke activism authenticity, this study proposes as a conceptual model, the woke activism authenticity framework, highlighting the interactions among the woke authenticity dimensions. The findings make conceptual contributions and offer several managerial recommendations toward woke authenticity and neutralizing the perception of inauthenticity.

A decentralized framework for device authentication and data security in the next generation internet of medical things

Internet of Things (IoT) applications have gained a huge momentum and have spanned across all domains adding innovations to the prevailing solutions. The IoT networks generate enormous data comprising meteorological information, patient critical body parameters, finance, logistics, location of a tracking object, etc. Security for sensitive data,such as patient body critical parameters from an attached monitoring device, streaming over IoT networks is vital and is a need of the hour. Data integrity and user (or device) authentication are crucial for building a trust-worthy communication network among the peers in IoT networks. Most networks still employ specific software encryption algorithms that provide considerable data security. But quantum computing has proved the vulnerability of computationally vigorous cryptographic algorithms. A decentralized and scalable framework for device authentication and data security is proposed in this paper based on blockchain platform and Physical Unclonable Functions (PUFs). An authentication protocol is developed using PUF-based cryptographic primitives. The PUF-based keys are hard to replicate and almost impossible to predict because of the randomness in the physical design and complex mathematical modeling of the system. Lightweight Smart contracts are used to facilitate role-based access control. Data privacy is preserved by storing the sensitive data off-chain. As a proof of concept, an IoT-based healthcare system based on Ethereum permissioned blockchain is developed using the proposed framework. The​ designed PUF exhibits 48.46% uniqueness and 2.38% reliability. A comparative analysis with existing similar models shows that the proposed approach is feasible and provides a scalable solution for device authentication and data security in resource-limited medical IoT networks.

A Secure Lightweight Hardware-Assisted Charging Coordination Authentication Framework for Trusted Smart Grid Energy Storage Units

A Secure Lightweight Hardware-Assisted Charging Coordination Authentication Framework for Trusted Smart Grid Energy Storage Units

‘Hard-Boiled’ Detectives in Spain and Mexico: The Ethical Reorientation of a Genre

Hard-boiled’ fiction arose in the early decades of the twentieth century, uncovering connections among crime, wealth and power, and exposing moral fissures within U.S. capitalism. After French publisher Gallimard marketed translations of American crime fiction as noir, international writers started adjusting the ethical framework of the original authors as part of their ‘glocal’ adaptation of a global genre to local circumstances. The present article pushes past ‘glocal’ analysis of noir to propose a ‘transnational’ relationship, adapting Paul Giles’ definition of ‘transnational’ practice in which international authors reflect the genre back upon its American roots in order to illuminate the ‘silences, absences and blindspots’ in the original ethical stance. The ‘misreading’ of noir also permits a ‘misrecognition’ of local circumstances, exposing moral fissures throughout different societies. This article shows how series by Manuel Vázquez Montalbán and Paco Ignacio Taibo II reveal ethical blindspots in American models by situating the detective within an emotional history of place (Barcelona for Vázquez Montalbán, Mexico City for Taibo II). Although these detectives ultimately cannot determine or perform the role of ethical citizen, their emotional-geographical bonds open up a critique of American ideals and pave the way for a reimagining of the ethical in the twenty-first century.

ToAM: a task-oriented authentication model for UAVs based on blockchain

The pervasive cooperation of a group of UAVs has attracted increasing attention due to the reduced cost and widespread availability. When working in an untrusted or adversarial environment, the mutual authentication of UAVs in the cooperative process is imperative. However, there are some major challenges, including changes in the network environment before and during task performing, and the weak connection network state faced by UAVs. Therefore, a novel task-oriented authentication model for UAVs based on blockchain (ToAM) is proposed, which divides UAVs authentication into group building authentication and intra-group authentication with a two-stage authentication framework. And two lightweight authentication protocols are presented, respectively, corresponding to two stages. Finally, analyses demonstrate that our model realizes secure and lightweight authentication function for the whole process of UAVs requisition and task performing.

To be (different) or not to be: The role of gender in constructing diplomatic speeches

With an increasing number of female political leaders worldwide, there has been a debate as to whether female politicians could bring alternative voices into politics, which has long been dominated by males. Some scholars highlight the role of gender identity, while others stress the masculine traits of political leadership. To contribute to this debate, we propose the idea of gender authenticity and argue that female politicians can strategically highlight their feminine traits and femininist consciousness even in a highly masculine environment. We compare the diplomatic speeches delivered by Hillary Clinton and John Kerry, who both served as secretary of state under former President Obama. Based on the framework of gender authenticity, we empirically examine how the remarks of these two top diplomatic officials differ in their topical, linguistic, and conceptual dimensions. While the political speeches of Clinton and Kerry behave similarly in terms of their linguistic styles, there are gendered differences in their topical and conceptual dimensions. The results suggest that Clinton pays closer attention to issues of women's rights and human rights, and that she also adopts a more feminine style in her interpretation of the concept of power.

A lightweight three factor authentication framework for IoT based critical applications

IoT is emerging as a massive web of heterogeneous networks estimated to interconnect over 41 billion devices by 2025, generating around 79 zettabytes of data. The heterogeneous network shall bring in a plethora of digital services leveraging cloud and communication technologies to drive smart city applications. As users access these services remotely in a ubiquitous environment over public channels, it becomes imperative to secure their communication. Both entity and message authentication emerge as a critical security primitive to thwart unauthorized access and prevent the falsification of messages. While researchers have given due attention to achieving mutual authentication between the subscriber (remote user) and gateway node (broker), the mutual authentication between the gateway node and an IoT sensor node is left to be desired. It could be done at the peril of a rogue or a shadow IoT device unauthorizedly joining an IoT-based network. Some of the widely used IoT-specific application layer protocols like constrained application protocol (COAP) and message queue telemetry transport (MQTT) protocol are not inherently equipped with adequate security safeguards. They, therefore, rely on underlying transport layer security protocols, which are highly computationally intensive. To address this issue, this paper proposes a three-factor authentication framework suitable for IoT-driven critical applications based upon identity, password and a digital signature scheme. The framework employs publish-subscribe pattern leveraging elliptical curve cryptography (ECC) and computationally low hash chains. The formal and informal security analysis shows that the framework is resistant to different types of cryptographic attacks. Furthermore, the automated validation performed with the Scyther tool verifies that there are no cryptographic attacks found on any of the claims stated in the proposed framework. Finally, a comparison of the framework security features, computational, and communication overheads is carried out with other existing protocols.

Secure Patient Authentication Framework in the Healthcare System Using Wireless Medical Sensor Networks.

Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent literature. The proposed protocol's security has been analyzed formally using BAN logic and ProVerif2.02 and informally using pragmatic illustration. Simultaneously, at the end of the paper, the performance analysis result shows a delicate balance of security with performance that is often missing in the current protocols.

Efficient Transmission of Watermarked Images with SC-FDMA

This paper studies the transmission of watermarked images on wireless SC-FDMA systems. Different watermarking schemes are provided in this frame work for security and authentication purposes.SC-FDMA is accomplished for broadband wireless communications, for its advantages of low PAPR and low sensitivity to carrier frequency offsets. The SC-FDMA system is used for communication as it is the uplink standard in coefficient mobile communication system. In this paper, two types of equalizers(MMSE and ZF) is used to mitigate the effect of channel fading and noise at different subcarriers mapping schemes(localized and distributed).Watermark extraction is performed at the receiver and an evaluation process for the quality of extracted watermarks is performed. Simulation experiments were established in MATLAB program R2014a version. Simulation results approved that the interleaved system provides better performance than that of the localized systems. In addition the system performance has better enhancement when using the ZF equalizer than the MMSE equalizer. The quality of the received of extracted watermarks is investigated with different metrics like PSNR, correlation coefficient (Cr) , SSIM and the histogram . Simulation results also demonstrate an efficient of image clarity at extraction process.

A Completely Distributed Blockchain Period Authentication Framework

The time capsule that would be opened in the future without third-party intervention was always a difficult issue. Although many researchers work on various systems, there are potential limitations, such as unreliable decryption period not entirely decentralised, which are difficult to estimate the needed data resources. In this post, we introduced a protocol and a safe cryptographic way to open a timely message in an advanced, decentralised environment to match in with several computing power conditions. The methodology also allows participants to gain extensive benefits of adding their computing resources, making our system more suited for applications in real life.

A Shared Blockchain Intelligent Public Protection Service Framework

With accelerated technical advancements in the IoT network, an intelligent scheme of public security is possible with the incorporation of heterogeneous data processing systems, with which public security services will collaboratively be provided. While this system design has always been pursued by IoT and Cyber internal environments, the provision of scalable and flexible IoT-based PLC frames in a legacy system is challenging. Traditional security techniques are sometimes founded on a tall structure which can be an in efficiency or a point let-down. Enthused through micro services also blockchain technologies, this article offers the Smart Public Security Architecture of Blockchain-Enabled Decentralized Micro services. A micro service-based authentication framework is implemented within a licence blockchain network for safe monitoring of data access in a PLC environment. Such functionality is divided into a single, intelligently organised package micro service which is dispersed at fog and edge node computing. A comprehensive experimental study found that the Blend MAS proposed would provide decentralised, scalable and secure intelligence gathering and access controls to the purchaser of IoT based PLC systems.

PUF enable lightweight key-exchange and mutual authentication protocol for multi-server based D2D communication

The momentous development in the technology of wireless communication has escalated the wide acceptance of Device to Device (D2D) communication among mobile devices. However, enormous requests of devices may curtail the effectiveness of conventional single-server based authentication frameworks. To overcome such limitation, multi-server authentication framework is introduced. This framework facilitates the devices to use multiple services granted from several servers through single registration via the registration center. In recent ages, numerous multi-server authentication protocols have been developed to securely facilitate the huge amount of devices via variety of servers. Nonetheless, most of these protocols are susceptible to various security threats and proved to be inefficient in terms of computation and communication overheads. In this article, we have proposed an Elliptic Curve Cryptography (ECC) based multi-server authentication framework using Physically Unclonable Function (PUF) for D2D communication. We have shown that our proposed protocol is more secure and robust using Random Oracle Model (ROM). The informal security analysis reveals that proposed protocol accomplishes all security needs of multi-server infrastructure. Further, we have separately computed all cryptographic operations for client, server and Registration Center (RC) to get desired experimental results. The performance analysis illustrates that introduced protocol attains compelling security improvements with least possible computation complexity compared to related protocols. Hence, the strong security features of our proposed protocol demonstrate its strength and make it viable for practical implementation.

A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications.

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction

Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a 'principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.

A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction

Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a ‘principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.

A broad review on non-intrusive active user authentication in biometrics.

Authentication is the process of keeping the user’s personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.