Blockchain

Cloud Computing, employed in various applications and services, refers to using computational resources as a service depending on customer needs via the Internet. The computing paradigm is built on data outsourcing to third-party-controlled data centers. Despite the significant developments in Cloud Services and Applications, various security vulnerabilities remain. This research proposes the EBBKG Model for Efficient Data Sharing in Cloud. For secure data sharing in the cloud, the approach combines BBKG with ABS. The method offers good data management that efficiently specifies the subsequent processing processes. The paradigm imposes encrypted access control, along with specific enhanced access capabilities. Secondly, the user's privacy may be adequately protected with a secure authentication paradigm that employs ABS to safeguard the user's private data. The key is optimized using BOA to enhance security and cloud providers and limit dangerous user threats using these implementations. Criteria like security, time complexity, and accountability govern the suggested method's effectiveness.

Blockchain technology is a decentralized and open database maintained by a peer-to-peer network, offering a “trustless trust” for untrusted parties. Despite the fact that some researchers consider blockchain as a bubble, blockchain technology has the genuine potential to solve problems across industries. In this article, we provide an overview of the development that Blockchain technology has had in 2018 and point out the challenges of deploying blockchain-based applications in the real world from a Human-Computer Interaction view. We propose that blockchain practitioners should design blockchain applications from users’ perspective, think about who the users are, and what they need. Furthermore, we also lay out possible future trends for blockchain based systems.

Since the bitcoin invention in 2008, blockchain technology has surpassed so numerous innovations that the pioneer networks such as Ethereum are adaptable to host a bunch of decentral information containing pictures, audio, video, domains, Etc., or even a metaverse versatile avatar. The transformation of tangible goods into virtual assets, known as the AR-utility of luxury products, and the intermixture of reality and virtuality organized a worldwide, semi-regulated, and decentralized marketplace for digital goods. Non-fungible tokens (NFTs) are doing a great help to artists worldwide, sharing diverse cultural outlooks by establishing a remote cross-cultural corporation potential and, at the same time, metamorphosizing the intermediary role and ceasing the necessity of a well-known art sale’s approval. Meanwhile, the cryptocurrency market has already acquired allocation and interest in the global domain, paving the way for a flourishing enthusiasm among entrepreneurs preoccupied with high-tech start-ups. In this qualitative survey based on participatory observation fieldwork, we shall decipher through self-exploration to investigate the ups and downs of the new cyberculture environment. Empirical encountering diverse Social Network Sites (SNS) and following the Cryptocurrency X(Twitter) trends, we have concluded that social media were the assembly line, producing success stories day and night which empowered a decentral market to surpass 14 billion worth of united states dollars. 

Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. However, a critical issue with Federated learning is the poisoning attack, which has severe consequences on the accuracy of the global model caused by the server's lack of access to raw data. To deal with this problem effectively, a distributed federated learning approach involving blockchain technology is proposed. Using the consensus mechanism based on reputation-based verifier selection, verifiers are selected based on their honest participation in identifying compromised clients. This approach ensures that these clients are correctly identified and their attack is ineffective. The proposed detection mechanism can efficiently resist the data poisoning attack, which significantly improves the accuracy of the global model. Based on evaluation, the accuracy of the global model is compared with and without the proposed detection mechanism that varies with the percentage of poisonous clients and different values for the fraction of poisonous data. In addition to the stable accuracy range of nearly 93%, the accuracy of our proposed detection mechanism is not affected by the increase of α in different values of β.

Blockchain is a technology that enables distributed and secure data structures for various business domains. Bitcoin is a notable blockchain application that is a decentralized digital currency with immense popularity and value. Bitcoin involves many concepts and processes that require modelling for better comprehension and development. Modelling is a technique that simplifies and abstracts a system at a certain level of detail and accuracy. Software modelling is applied in Model-Driven Engineering (MDE), which automates the software development process using models and transformations. Domain-specific languages (DSLs) are languages that are customized for a specific domain and offer intuitive syntax for domain experts.  To address the need for specialized tools for Bitcoin blockchain modelling, we propose a novel Unified Modelling Language (UML) profile that is specifically designed for this domain. UML is a standard general-purpose modelling language that can be extended by profiles to support specific domains. A meta-model is a model that defines the syntax and semantics of a modelling language. The proposed meta-model, which includes stereotypes, tagged values, enumerations, and constraints defined by Object Constraint Language (OCL), is defined as a UML profile. The proposed meta-model is implemented in the Sparx Enterprise Architect (Sparx EA) modelling tool, which is a widely used tool for software modelling and design. To validate the practicality and effectiveness of the proposed UML profile, we developed a real-world case study using the proposed meta-model and conducted an evaluation using the Architecture Tradeoff Analysis Method (ATAM). The results showed the proposed UML profile promising.

In terms of storage and consumption, blockchain technology is poised to transform the way we manage healthcare data. The primary goal is to empower individuals to take charge of their health records, allowing them to become independent of the institutions or organizations they use. Elec-tronic Health Records (EHRs) can be tracked in a novel and unique way through blockchain tech-nology and smart contracts. This technology can give patients more control over their data. Health practitioners and institutions, such as hospitals, may be granted access to patient data controlled by other organizations. This research highlights how blockchain technology can be used to manage EHRs while improving operational efficiency through process simplification and transparency. Additionally, the study proposes an architecture for managing and sharing healthcare data across enterprises. The suggested approach could significantly reduce the time required to transfer patient data among various health organizations while lowering overall costs.

Today, the Internet of Things is a widely recognized phenomenon that generates a significant amount of data and connects many devices. Many products are incorporating electronic components to facilitate their integration and interaction with the Internet. Scalable and efficient trust management systems are required to maintain network reliability, considering the increasing number of IoT devices and generated data. In order to enable scalable trust management in social IoT, this paper presents a sharding-based scalable trust management approach that combines social interactions with smart contract functionality. Through the division of transaction state into smaller segments and the enhancement of trust value propagation among connected devices, sharding techniques in blockchain can offer scalable trust management protocols. When implementing the model on the Hyperledger Fabric platform, we carried out a thorough evaluation. The model calculates trust in terms of trust convergence and success rate efficiently. We have conducted several tests to evaluate the scalability of the model. To boost it, we have also implemented the state sharding. We also conducted a study to highlight the advantages of the sharding strategy on the scalability of the model. The results demonstrate that using shards significantly improves trust management capacity on the blockchain. The proposed method demonstrates the potential application of sharding in blockchain-based Trust Management (TM) for scalable trust management in SIoT.

Artificial intelligence (AI) has a huge potential to transform industry and society. Its benefits are widely recognized, and it has become a tool of strategic importance for the European Union and a major driver of economic development. However, as with any technological development, it also comes with challenges that must be assessed and, if necessary, addressed by policy makers and businesses. Artificial intelligence has been a big challenge for the insurance industry for decades and is creating fundamental changes in the way this industry operates. The application of artificial intelligence to eliminate repetitive tasks and improve efficiency is visible in the market of porter insurance and commercial insurance. On the contrary, personalization of insurance through artificial intelligence is limited to personal lines and SME business. However, its application in commercial insurance including various marine levels will be widely involved in the near future. It is likely that AI will have a broad impact on the insurance value chain, from underwriting and claims management to distribution and customer service to asset management. As a result, insurance executives should be familiar with the new technologies involved in this change and how artificial intelligence can help organizations produce innovative products, gather valuable insights from new sources, streamline processes, and improve customer service. The purpose of this article is to make interested people and people involved in the insurance matter familiar with the potential benefits related to artificial intelligence applications and to motivate academics to study controversial topics in this field. For this purpose, we have only referred to the study of existing articles and research regarding the use of artificial intelligence in the insurance industry, especially marine insurance, and its effects on the compensation of losses caused by it.