ABSTRACT
Because of the Covid-19 pandemic, urgent surging demand for healthcare products such as personal protective equipment (PPE) has caused significant challenges for multi-tier supply chain management. Although a given firm may predominantly focus on an arms-length solution by targeting the first-tier supplier, the firm can still struggle with extended multi-tier suppliers it cannot choose which use unsustainable practices. One key goal is compliance across various dimensions with production, environmental and labour standards across the multi-tier supply chain, a goal that blockchain technology can be applied to manage. Based on a comprehensive literature review, this research develops a system architecture of blockchain-based multi-tier sustainable supply chain management in the PPE industry designed to identify and coordinate standards in production and social and environmental sustainability in multi-tier PPE supply chains. The architecture was validated by theoretical basis, expert opinions and technical solutions. We conclude with managerial implications for implementing blockchain technology to advance sustainable multi-tier supply chain practices.
ABSTRACT
Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends - as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack. © 2023 Marie Tcholakian et al.
ABSTRACT
The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.
ABSTRACT
Wireless body sensor network (WBSN) is an interdisciplinary field that could permit continuous health monitoring with constant clinical records updates through the Internet. WBAN is a special category of wireless networks. Coronavirus disease 2019 (COVID-19) pandemic creates the situation to monitor the patient remotely following the social distance. WBSN provides the way to effectively monitor the patient remotely with social distance. The data transmitted in WBSN are vulnerable to attacks and this is necessary to take security procedure like cryptographic protocol to protect the user data from attackers. Several physiological sensors are implanted in the human body that will collect various physiological updates to monitor the patient's healthcare data remotely. The sensed information will be transmitted wirelessly to doctors all over the world. But it has too many security threats like data loss, masquerade attacks, secret key distribution problems, unauthorized access, and data confidentiality loss. When any attackers are attacking the physiological sensor data, there is a possibility of losing the patient's information. The creation, cancellation, and clinical data adjustment will produce a mass effect on the healthcare monitoring system. Present-day cryptographic calculations are highly resistant to attacks, but the only weak point is the insecure movement of keys. In this paper, we look into critical security threats: secure key distribution. While sharing the secret key between communicating parties in the wireless body sensor networks in the conventional method like via phone or email, the attackers will catch the private key. They can decrypt and modify more sensitive medical data. It can cause a significant effect like death also. So need an effective, secure key distribution scheme for transmission of human body health related data to medical professional through wireless links. Moreover, a new enhanced BB84 Quantum cryptography protocol is proposed in this paper for sharing the secret key among communicating parties in a secure manner using quantum theory. Besides, a bitwise operator is combined with quantum concepts to secure the patient's sensed information in the wireless environment. Instead of mail and phone via sharing secret key, quantum theory with the bitwise operator is used here. Therefore, it is not possible to hack the secret key of communication. The body sensor's constrained assets as far as battery life, memory, and computational limit are considered for showing the efficiency of the proposed security framework. Based on experimental results, it is proven that the proposed algorithm EBB84QCP provides high secure key distribution method without direct sharing the secret key and it used the quantum mechanism and bitwise operator for generating and distributing secret key value to communicating parties for sensitive information sharing in the wireless body sensor networks.
ABSTRACT
The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients' worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients' healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes. © 2023 IEEE.
ABSTRACT
As the number of MS Teams, Zoom, and Google Meet users increases with online education, so do the privacy and security vulnerabilities. This study aims to investigate the privacy, security, and usability aspects of few tools that are frequently used for educational purposes by Bangladeshi universities. Consumer security, privacy, and usability are also concerns when it comes to online-based software. This study assesses the most commonly used tools that are used for online education based on three important factors: privacy, security, and usability. Assessment factors concerning the privacy, security, and usability aspects are initially identified. Afterwards, each of the applications was assessed and ranked by comparing their characteristics, functionalities, and terms and conditions (T&C) in contradiction of those factors. In addition, for the purpose of additional validation, a survey was carried out with 57 university students who were enrolled at one of several private universities in Bangladesh. Microsoft Teams, Zoom, and Google Meet have been ranked based on an evaluation of their security, privacy, and usability features, which was accomplished through the use of a knowledge base and a user survey. © 2022 IEEE.
ABSTRACT
Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).
ABSTRACT
COVID‐19, which is a global problem, affects the all supply chains throughout the world. One of the supply chains most affected by COVID‐19 is food supply chains. Since the sustainable food supply chain processes are complex and vulnerable in terms of product variety, it has been negatively affected by the operational effects of COVID‐19. While the problems experienced in the supply chain processes and raw material constraints caused stops in production, the importance of new business models and production approaches came to the fore. One of the issues of increasing importance is the adoption of reverse logistics activities in sustainable food supply chains and increasing the resilience of food supply chains by integrating blockchain technology into processes. However, adapting blockchain technology to increase the resilience of reverse logistics activities in the food supply chain has advantages as well as risks that need to be considered. Therefore, it is aimed to determine these risks by using fuzzy synthetic evaluation method for eliminating the risks of blockchain adaptation for flexible reverse logistics in food supply chains to increase resiliency. The novelty of this study is that besides discussing about the benefits of BC‐T, it is to identify the risks it can create, to eliminate these risks and to guide the establishment of resilience in reverse logistics activities of SFSCs. According to results, the risks with the highest value among the subrisks are determined as data security risks. Data management risks are calculated as the risk with the highest value.
ABSTRACT
The circular economy (CE) has gained importance in the post-COVID-19 pandemic recovery. Businesses, while realising the CE benefits, have challenges in justifying and evaluating the CE benefits using available performance measurement tools, specifically when considering sustainability and other non-traditional benefits. Given the rising institutional pressures for environmental and social sustainability, we argue that organisations can evaluate their CE implementation performance using non-market-based environmental goods valuation methods. Further, the effectiveness of the CE performance measurement model can be enhanced to support supply chain sustainability and resilience through an ecosystem of multi-stakeholder digital technologies that include a range of emerging technologies such as blockchain technology, the internet-of-things (IoT), artificial intelligence, remote sensing, and tracking technologies. Accordingly, a CE performance measurement model (CEPMM) is conceptualised and exemplified using seven COVID-19 disruption scenarios to provide insights that can be addressed through CE practices. Analyses and implications are presented along with areas for future research.
ABSTRACT
Information is important market resource. High-quality information is beneficial to increase enterprise's reputation and reduce consumer's verification cost. This paper constructs a two-layer dynamic model, in which enterprises simultaneously conduct price and information game. The goal of profit maximization integrates two types of games into one system. The complex evolution of the two-layer system are studied by equilibrium analysis, stability analysis, bifurcation diagram, entropy and Lyapunov exponent. It is found that improving the information quality through regulations will increase involution and reduce stability of the market. Then, the block chain technology is introduced into the model for improving information quality of the market. It is found that increasing enterprises' willingness to adopt block chain can improve the information quality quickly and effectively, and that is verified by entropy value. Therefore, the application and promotion of new technologies are more effective than exogenous regulations for improving information quality in market.
ABSTRACT
PurposeThe COVID-19 pandemic has had a significant and worldwide influence on healthcare delivery, and it has significantly increased the pace at which digital technology is being used. Blockchain, one of these developing digital technologies, is distinguished by a number of properties. This study focuses on a blockchain-enabled healthcare supply chain. The purpose of this work is to investigate how blockchain technology (BCT) benefits the performance of healthcare supply chain management (HSCM).Design/methodology/approachThe present study is based on the empirical research. Blockchain Technology (BCT), Healthcare Sustainable Supply Chain Practices (HSSCP), Healthcare Supply Chain Performance (HSCP) and Stakeholders' Involvement (SI) practices are identified from the literature review and hypotheses are framed to check their interrelationship. For testing of hypothesis, a questionnaire was developed. Data collection was done by healthcare professionals via Google docs. The IBM SPSS version 22.0 was used to analyze the data and IBM SPSS AMOS 22.0 software was used for the development of structural modal. The data was collected through the Google form from the stakeholders of healthcare sector and analyzed through Structural Equation Modelling.FindingsThis research is focused on adoption of BCT enabled Healthcare Sustainable Supply Chain to improve HSCP. From the result, it had been found that BCT is positively effecting the stakeholder's involvement (SI) and HSSCP practices. Cumulatively, they positively impact the performance of HSCP. From this study, it is found that adoption of BCT enabled Healthcare Sustainable Supply Chain succours to combat COVID-19 situation.Originality/valueThis study attempts to show the potential benefits of the adoption of BCT enabled HSSCP to improve HSCP.
ABSTRACT
The coronavirus pandemic (COVID-19) threatens people's health. During the COVID-19 outbreak, people are encouraged to wear masks to reduce the spread of the virus. With the strong demand for masks, it has come a boom in counterfeit production. Combating counterfeit masks is vital and urgent to reduce the risks for public health. Motivated by the actual practices during the COVID-19, we examine how quality inspection and blockchain adoption help combat counterfeit masks. We find that quality inspection may not be always effective, as the government will tolerate the presence of counterfeit masks if the presence of the counterfeits is not significant. Comparing quality inspection with blockchain adoption, when the spread of COVID-19 is mild, authentic mask sellers may be encouraged to use the blockchain technology, which can increase their profits and reduce the social health risk. Furthermore, we extend our model to investigate the impacts of endogenous quality. Both quality inspection and blockchain adoption can induce low-quality mask sellers to enhance thequality level. When the number of counterfeit masks is increasing, encouraging the high-quality mask sellers to adopt the blockchain technology is effective to reduce social health risk when the spread of the coronavirus is rapid.
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The COVID-19 pandemic led to an acceleration of digitalisation in healthcare institutions, not only in the medical field but also within non-medical, which includes facility management (FM). FM organisations are increasingly confronted with the need to digitally transform their operations and to implement new digital technologies. This paper aims at providing scholars and professionals with an overview of the various digital technologies and systems that are relevant in shaping the digital transformation. An integrative literature review has been chosen, as it provides a systematic approach to map, collate and report on key findings and concepts from the literature for researchers and practitioners. Overall, 33 articles were systematically reviewed. 22 different digital technologies and systems were identified in the literature and were added to so-called technology clusters. From all the described technologies, Building Information Modelling (BIM) is most prominently cited. Furthermore, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), Digital Twins (DT), and Blockchain technologies are commonly found. Additional technologies and systems mentioned in the literature, though not further detailed, were also added within a separate cluster. This study also discusses the implications for the digital transformation which is important when introducing novel digital technologies in healthcare organisations. It is argued that FM in healthcare needs to focus on integrating technologies, both at a technological level, and particularly at an organisational and interorganisational level.
ABSTRACT
PurposeSmart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approachA game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.FindingsThe numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implicationsThis work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implicationsBy using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/valueThis work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) effect has made telecommuting and remote learning the norm. The growing number of Internet-connected devices provides cyber attackers with more attack vectors. The development of malware by criminals also incorporates a number of sophisticated obfuscation techniques, making it difficult to classify and detect malware using conventional approaches. Therefore, this paper proposes a novel visualization-based malware classification system using transfer and ensemble learning (VMCTE). VMCTE has a strong anti-interference ability. Even if malware uses obfuscation, fuzzing, encryption, and other techniques to evade detection, it can be accurately classified into its corresponding malware family. Unlike traditional dynamic and static analysis techniques, VMCTE does not require either reverse engineering or the aid of domain expert knowledge. The proposed classification system combines three strong deep convolutional neural networks (ResNet50, MobilenetV1, and MobilenetV2) as feature extractors, lessens the dimension of the extracted features using principal component analysis, and employs a support vector machine to establish the classification model. The semantic representations of malware images can be extracted using various convolutional neural network (CNN) architectures, obtaining higher-quality features than traditional methods. Integrating fine-tuned and non-fine-tuned classification models based on transfer learning can greatly enhance the capacity to classify various families of malware. The experimental findings on the Malimg dataset demonstrate that VMCTE can attain 99.64%, 99.64%, 99.66%, and 99.64% accuracy, F1-score, precision, and recall, respectively. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.
ABSTRACT
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
ABSTRACT
Corporations and small/medium enterprises (SMEs) are subject to a variety of external and internal pressures that often lead to changes in their corporate governance structures and accounting/reporting systems. The environment in which these organizations are collocated has undergone a deep process of change, due to the COVID-19 pandemic, climate change, the blockchain, and the energy industry crisis. Business activities represent a critical and a vital component of human existence across the globe—one that is not restricted to a financial standpoint—and their impact on societal, environmental and animal conditions is now undisputed. However, these activities are frequently coupled with allegations of their being the actual causes of those disruptions and collapses that persist in escaping the scrutiny of international governments. For the effective delivery of sustainable business activities, the concepts of governance and accountability are crucial, and the future of the inhabitants of planet Earth is arguably dependent on the ability of corporations (through their entire value chain) to govern themselves well and to demonstrate accountability to their many stakeholders. This should be achieved through the adoption of good governance standards which are well accepted, and that are globally harmonised with ‘Environmental, Social and Governance' (ESG) reporting tools that are able to strategically assess and evaluate risk exposure and provide forward-looking information. In this critical context, few studies have actually examined these issues thoroughly, and, because the findings of those studies have been contradictory, there is still no definitive understanding of the causes of weak accounting and reporting tools for ESG dynamics under conditions of disruption. A systematic literature network analysis (SLNA) is used in this study to examine the evolution of the ESG reporting research domain based on existing relationships (e.g., aggregation, cross-citations and isolation) among authors contributing to the field. The findings demonstrate the current state of the art, disclosing interesting and timely future research directions. Furthermore, this study employs a novel approach known as SLNA to conduct the analyses, confirming its efficacy as a tool for dynamic analysis also within the field of sustainability accounting research.
ABSTRACT
Globalization has prompted enterprises worldwide to increasingly seek the optimal supply chain configuration. However, outsourcing, shortened product life cycles, and a reduced supply base severely weaken supply chain risk tolerance. With the emergence of blockchain, enterprises see an opportunity to mitigate supply chain risks. The purpose of our research is to explore supply chain managers' intention to adopt blockchain technology from the perspective of supply chain risk management. Using a survey sample of 203 managers in China and the USA, we explored the impact of four perceived benefits of blockchain technology on supply chain risk resistance by extending the technology acceptance model. The results show that the traceability, transparency, information sharing, and decentralization of blockchain can enhance the perceived usefulness of blockchain in supply chain resilience and responsiveness, and the ability to withstand disruption risks and supply and demand coordination risks encountered in the supply chain, thus promoting the adoption of the technology. In addition, the relationships between supply chain resilience and blockchain technology adoption and between supply chain responsiveness and blockchain technology adoption are more salient for managers with high levels of uncertainty avoidance.
ABSTRACT
The main purpose of our study is to form a demonstration model of the main processes for introducing digital technologies into the human resources management system for engineering enterprises. Digital transformations are associated with management changes, which are based on the technologies of the Internet of Things, artificial intelligence, blockchain, machine learning, Industry 4.0, Big Data in all spheres of public life. Investing in human capital has always been considered a productive investment. The digital economy has increased the urgency of increasing labor productivity through the transformation of human governance mechanisms. The main and key processes of the introduction of digital technologies in the human resources management system of the engineering enterprise were considered. The digitalization of society has radically changed people's lives and opened up new opportunities in the field of human resources management. The digital transformation of the human resources system affects all types of businesses, from large corporations to small micro-firms. As a result, the key stages and processes of implementation of digital technologies in the human resources management system of the enterprise were presented. The research methodology consisted of the application of modeling and graphical display methods.