Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

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.

Development of a Decentralized Web App (D-App) for Covid-19 using AI

A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.

COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3-layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images;edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that has no correlation with each other. The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset, and the findings that we have obtained are quite encouraging. © 2023 Tech Science Press. All rights reserved.

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

D-insta: A Decentralized Image Sharing Platform

Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

D-Test: Decentralized Application for Preventing Fake COVID-19 Test Certificate Scam by Blockchain

More than 6 million people have lost their lives due to COVID-19 across the world (Ghatkopar in Fake negative COVID-19 certificate scam unearthed, 2019, [2];WHO (World Health Organization) in https://covid19.who.int/table, [3]). Recently, fake COVID-19 test certificate scams have spiked up drastically and become one of the reasons for the spread of COVID-19. In light of the current scenario, this paper proposes a decentralized approach called, "D-Test” for COVID-19 testing which allows the hospital and the general public to register themselves at a common platform which follows the concept of CIA triad (Confidentiality, Integrity, and Availability) and allows users to register without any fear of data breach. This platform registers users based on smart contract and enables the user to do the following once registered successfully: (a) Book Testing Slot, (b) Find nearby registered testing laboratories, and c) Generate the COVID-19 reports which could be imported and exported as and when required by the user. This has a higher value of trust because the source of the report can be traced back since usage of Blockchain prevents the likelihood of data tampering by an entity. This framework could help the government(s) keep track of distributing authentic COVID-19 testing certificates, prevent the fake COVID-19 testing certificate scams, and will speed up the process of verifying the users' test reports, thereby saving lives of many citizens around the world. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Telework as a Means of Organizational Identity Change: Investigating Japanese Collectivist Culture in an ICT Company

Due to the COVID-19 pandemic, numerous companies have implemented telework to balance business continuity with employees' safety. However, telework was not a widespread phenomenon in Japan until recently. Why is a geographically decentralized workstyle not as widespread in Japan as in other nations? In a previous study, based on collected samples from 529 Japanese companies, we used statistical analyses and clarified that the communication style aligned with Japanese collectivism seriously hinders telework. The present study used qualitative analysis to investigate how the communication style associated with Japanese collectivist culture hinders the introduction of telework. Finally, we concluded that if a non-Western cultural company introduces telework, it should remove the negative aspects of the communication style associated with collectivist culture to bring about organizational change that leads to a new organizational identity. © 2022 IEEE Computer Society. All rights reserved.

BUILDING A SUSTAINABLE SHARING ECONOMY MODEL BASED ON A VIRTUAL COMMUNITY CURRENCY.

After the economic crisis of 2008, the need for solutions that introduce alternative forms of cooperation between economic actors increased greatly. At the same time, concerns for the environment have intensified, and the integration of environmental considerations in economic activities has become increasingly important. As a response, peer-to-peer economy and peer-to-peer payment systems, among other things, have emerged. Compared with previous economic crises, the COVID-19 pandemic has posed new challenges for everyone, which could lead to the intensification of alternative path-finding processes. The ecological problems the we face mean that the aim should be to go beyond the restoration of previous economic mechanisms prioritising ecological sustainability. In this study, the authors' aim was to present the elements of a novel solution concept that is based on the hypothesis that a digital community currency created through smart contracts can promote genuine practices of sharing as opposed to the currently operating platforms' profit-oriented approach. (English) [ FROM AUTHOR] A 2008-as gazdasági válságot követően megnőtt az érdeklődés az olyan üzleti modellek iránt, amelyek a szereplők közötti együttműködés alternatív formáit biztosítják. Ezzel egyidejűleg a környezetszennyezéssel kapcsolatos aggodalmak is felerősödtek és a környezeti szempontok gazdasági tevékenységekbe történő integrálása egyre fontosabbá vált. Erre válaszul jelentek meg többek között a közösségi gazdasági és a peer-to-peer fizetési rendszerek. A COVID-19 világjárvány hatásai a korábbi gazdasági válságokhoz képest is új kihívások elé állítják a gazdasági szereplőket, ami az alternatív útkeresési folyamatok ismételt előtérbe kerüléséhez vezethet. Az előttünk álló ökológiai problémák miatt azonban a járványt követően a korábbi gazdasági mechanizmusok helyreállításán túl a célnak egy az ökológiai lábnyom csökkentését elősegítő gazdasági modell kiépítésének kell lennie. Ebben a tanulmányban a szerzők célja egy újszerű megoldási koncepció elemeinek bemutatása, amely azon a hipotézisen alapul, hogy az intelligens szerződések révén létrehozott digitális közösségi valuta elősegítheti a megosztás valódi gyakorlatát, szemben a jelenleg működő sharing economy platformok profitorientált megközelítésével. (Hungarian) [ FROM AUTHOR] Copyright of Vezetéstudomány / Budapest Management Review is the property of Corvinus University of Budapest and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Towards Decentralized Autonomous Digital Signatures Using Smart Contracts

Owing to the spread of COVID-19, the digitalization of various services is rapidly being promoted. In particular, online services such as obtaining a digital certificate (e.g., digital signature) from an authority are becoming increasingly important. Therefore, systems that can autonomously generate digital signatures are urgently required. However, the autonomous generation of signatures is difficult because the secret key for signatures must be strictly managed. Moreover, a decentralized autonomous systems should be publicly verifiable. Thus, schemes that preclude strict control of the secret key are desirable. In this study, we propose a new decentralized scheme that autonomously generates a digital signature without a secret key, using blockchain-based smart contracts. The fundamental concept behind our scheme is to eliminate secret keys by leveraging the closed nature of the processing operations of smart contracts within the blockchain;thus, the process of generating signatures and their output values satisfies the condition of immutability. Finally, we perform a security evaluation and feasibility study of our proposed scheme and show that it works securely on the Ethereum blockchain. © 2023 IEEE.

Privacy-Preserving Federated Learning in Healthcare

Federated learning (FL) has received great attention in healthcare primarily due to its decentralized, collaborative nature of building a machine learning (ML) model. Over the years, the FL approach has been successfully applied for enhancing privacy preservation in medical ML applications. This study aims to review prevailing applications in healthcare for the future landing FL application. We identified the emerging applications of FL in key healthcare domains, including COVID-19, brain tumor segmentation, mammogram, sleep quality prediction, and smart healthcare system. Finally, we discuss privacy concerns in federated setting and provide current methods to increase the data privacy capabilities of FL. © 2023 IEEE.

A public-private collaboration model of supply chain resilience to unpredictable disruptions: an exploratory empirical case study of medical mask production and distribution

As the COVID-19 pandemic continued unabatedly, many global supply chains involved in manufacturing and distributing personal protective equipment often failed to meet surge demand due to production capacity limits. Before the COVID-19 pandemic, the existing medical mask supply chain in Taiwan was decentralized, but immediately following the outbreak in 2020, the government of Taiwan established a centralized virtual company that integrated production, distribution, and sales. We use an exploratory empirical case study to gain insights into Taiwan's innovative public-private collaboration and the relationship between collaborative activities and supply chain resilience. This paper examines how a ten-fold growth, from 1.88 million to 20 million, in the daily production of medical masks, and their equitable distribution was achieved within four months of the onset of the COVID-19 pandemic. The results indicate that the public-private collaboration through a government-led centralized supply chain mitigated the impacts of unpredictable disruptions, built supply chain resilience, and ensured mask availability to the public. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Recent Advances of Blockchain and Its Applications

Blockchain is an emerging decentralized data collection, sharing, and storage technology, which have provided abundant transparent, secure, tamper-proof, secure, and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-enabled applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus and storage mechanisms and communication schema in general blockchain systems. Then extensive literature review is conducted on blockchain-enabled Internet of Things (IoT), edge computing, federated learning, and several emerging applications including healthcare, COVID-19 pandemic, online social network, and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges, and opportunities in both academia and industry. © 2020 Tsinghua University Press.

Dynamic Computer-Aided Process Control with Computer Vision for Industry 4.0

Because of health concerns and factory operational scale backs during the recent COVID-19 pandemic, we now need factory automation more than ever to maintain our productivity. However, most of our factories cannot operate remotely, and none can function without considerable human input and oversight. Trying to automate our factory highlights gaps in our technology, as it seems far behind our expectations, needs, and vision. Thus, this paper aims to fill this gap by showing how we have developed practical methodologies and applied technology to enhance legacy factories and their equipment. Specifically, we present the ORiON Production Interface (OPI) unit to run the factory as a smart networked edge device for virtually any machine or process. We have also implemented various computer vision algorithms in the OPI unit to detect errors autonomously, make decentralized decisions, and even control the quality. Although Industry 4.0 is a known concept to equip our factory to see, understand, and predict, we know that many machines today are closed source and cannot even communicate, let alone join a network. This research provides a workable solution to realize Industry 4.0 truly in existing factories with legacy equipment. Experimental results show that this system has a variety of applications, including process monitoring, part positioning, broken tool detection, etc. This novel intelligent networked system can enable our factories to be more innovative and responsive. It also allows for remote operations that can be unattended or lightly tended—a trend needed for the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Using the Ethereum Blockchain to Secure a Crowdfunding System in the Real Estate Sector

The covid-19 crisis has severely affected the dynamics of the real estate sector, which is facing various financial and structural problems. The current real estate world is complicated by the lack of transparency in transactions such as rental, purchase, and sale, and it does not reach the level of confidentiality and authenticity of operational data. In addition, real estate financing brings together several players such as banks, notaries, and others, which makes the acquisition of real estate very expensive. With the advent of blockchain technology, many fields such as finance, accounting, and real estate have received a positive impact using the benefits of this technology. This article aims to reorganize real estate into a next-generation digitized system based on blockchain technology, by proposing a crowdfunding model that aims to eliminate intermediate, costs. Moreover, this model can allow to customers, who do not have immediate financing, the possibility of acquiring real estate. We also present the implementation of this model through smart contracts and the blockchain to set up a decentralized platform that ensures the security, traceability, and transparency of transactions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics. © 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

D-insta: A Decentralized Image Sharing Platform

Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Contracts For NGOs and Startups using Blockchain

This paper proposes and emphasizes the requirement of an Blockchain based smart contract for NGO's and startup crowdfunding in the present circumstances. It also highlights the need of an online financial system for indigenous NGO's and seed fund utilization of startups. Conventionally, most charity organizations make use of hard cash for settling its transactions making the process less transparent. However, due to the COVID-19 pandemic, financial system has been largely affected. In this case an online financial transaction cum procurement portal would be crucial for the candidates applying relief in remote locations. The system analyses their eligibility based on their Curriculum Vitae (CV). Proposed system uses Ethereum based smart contract and Truffle Box to build a complete Dapp (decentralized application). Authors have used MetaMask Extension as a cryptocurrency wallet and Ganache blockchain to develop, deploy and test the decentralized application. © 2022 IEEE.

Patient and Healthcare Professional Insights of Home- and Remote-Based Clinical Assessment: A Qualitative Study from Spain and Brazil to Determine Implications for Clinical Trials and Current Practice.

INTRODUCTION: The SARS-CoV-2 virus pandemic has accelerated the growing trend towards using home- and remote-based medical testing (H/RMT). The aim of this study was to gather insights and explore the opinions of patients and healthcare professionals (HCPs) in Spain and Brazil regarding H/RMT and the impact of decentralised clinical trials. METHODS: This qualitative study consisted of in-depth open question interviews of HCPs and patients/caregivers followed by a workshop that aimed to determine the advantages and barriers to H/RMT in general, and in the context of clinical trials. RESULTS: There were 47 participants in the interviews (37 patients, 2 caregivers, 8 HCPs) and 32 in the validation workshops (13 patients, 7 caregivers, 12 HCPs). The main advantages for the use of H/RMT in current practice were the comfort and convenience, the ability to improve the relationship between HCPs and patients and personalise patient care, and the increased patient awareness towards their disease. Barriers to H/RMT included accessibility, digitalisation, and the training requirements for both HCPs and patients. Furthermore, according to the Brazilian participants, there is a general distrust in the logistical management of H/RMT. Patients indicated that the convenience of H/RMT did not influence their decision to participate in a clinical trial, with the main reason for participating in a clinical trial being to improve health; however, H/RMT in clinical research does aid adherence to the long-term follow-up associated with trials and provides access to patients living far from the clinical sites. CONCLUSION: Insights from patients and HCPs suggest that the advantages of H/RMT may outweigh the barriers, and that social, cultural and geographical factors and the HCP-patient relationship are critical aspects to be considered. Moreover, the convenience of H/RMT does not appear to be a driver for participating in a clinical trial but can facilitate patient diversity and study adherence.

Decentralised clinical trials in multiple sclerosis research.

Randomised controlled trials (RCTs) play an important role in multiple sclerosis (MS) research, ensuring that new interventions are safe and efficacious before their introduction into clinical practice. Trials have been evolving to improve the robustness of their designs and the efficiency of their conduct. Advances in digital and mobile technologies in recent years have facilitated this process and the first RCTs with decentralised elements became possible. Decentralised clinical trials (DCTs) are conducted remotely, enabling participation of a more heterogeneous population who can participate in research activities from different locations and at their convenience. DCTs also rely on digital and mobile technologies which allows for more flexible and frequent assessments. While hospitals quickly adapted to e-health and telehealth assessments during the COVID-19 pandemic, the conduct of conventional RCTs was profoundly disrupted. In this paper, we review the existing evidence and gaps in knowledge in the design and conduct of DCTs in MS.

The potential of decentralised wastewater treatment in urban and rural sanitation in South Africa: lessons learnt from a demonstration-scale DEWATS within the eThekwini Municipality

The design principles of decentralised wastewater treatment systems (DEWATS) make them a practical sanitation option for municipalities to adopt in fast-growing cities in South Africa. Since 2014, a demonstration-scale DEWATS with a modular design consisting of a settler, anaerobic baffled reactor (ABR), anaerobic filter (AF), vertical down-flow constructed wetland (VFCW) and horizontal flow constructed wetland (HFCW) has been in operation in eThekwini. A performance evaluation after the long-term operation was undertaken in 2019 by comparing the final effluent with national regulatory requirements. Despite limitations in characterising the raw wastewater, a comparison of the settler and final effluent quality indicated high (≥ 85%) removal efficiencies of total chemical oxygen demand (CODt), ammonium-N (NH4-N) and orthophosphate-P (PO4-P), 75% removal of total suspended solids (TSS) and 83.3% log10 removal of Escherichia coli. Lack of exogenous and endogenous carbon and high dissolved oxygen (DO) concentrations (> 0.5 mg·L−1) inhibited denitrification in the HFCW, resulting in 12.5% of the effluent samples achieving compliance for nitrate-N (NO3-N). Moreover, mixed aggregate media and low residence times in the HFCW may have also contributed to poor NO3-N removal. During the COVID-19 lockdown, an unexpected shutdown and subsequent resumption of flow to the DEWATS indicated a 16-week recovery time based on achieving full nitrification in the HFCW. Although design modifications are necessary for the HFCW, the installation of urine diversion flushing toilets at the household level will reduce the nutrient loading to the DEWATS and potentially achieve fully compliant effluent. Alternatively, the application of two-stage vertical flow constructed wetlands to improve denitrification should also be explored in the South African context. With an improved design, DEWATS has the potential to fill the gap in both urban and rural sanitation in South Africa, where waterborne sanitation is still desired but connections to conventional wastewater treatment works (WWTWs) are not possible. © The Author(s) Published under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0).