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The identity management (IM) solutions are designed for facilitating and managing in digital field that identifies to perform the authentication operation which has been widely used for the real-world applications. The blockchain-based IM solutions are provided which allows the user to take the control of his or her identity. The existing blockchain-based IM models and based identified the research gap and other opportunities that will improve the present research work. Also, the information for owners who are trustworthy, traceable, secure ownership for the medical data that created seriousness in the security issues. The techniques and systems were centralized made difficult for sharing the medical data that have increased the single point of failure. The blockchain technology enabled the transparent, dependable, and auditable computing in various industries that used the decentralized network in the public ledger. The main objective is to exchange data in a safe and secure manner by using unalterable logs. To achieve and to improve the transparency, security and privacy, the verifiable digital signature with controlled blockchain enabled data records (DVS-CBDR) is introduced. Performance study of the proposed system has been assessed by a number of studies, examining throughput (170 tx/s), transaction response time (21 s), latency (5 s), memory usage (200 MB), and CPU utilization (98 Hz) which is better than patient-controlled blockchain enabled electronic health records for healthcare (PcBEHR). [ABSTRACT FROM AUTHOR]

Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence. [ABSTRACT FROM AUTHOR]

Blockchain, essentially a distributed and immutable database, has emerged as a potential solution to governance problems of accountability, transparency, and trust in the construction industry. In terms of construction quality management (CQM) applications, previous works mainly took a technical perspective, and little is known about the potential of blockchain-based governance for quality opportunism. Moreover, we have seen very few successful blockchain implementations in construction practices. Stakeholders meet the trade-off between potential benefits and risks in blockchain adoption decisions. Previous works mainly focused on barrier analysis, neglecting the dynamic interactions of stakeholders' strategic behaviors on adopting blockchains. Against this backdrop, this research aims to systematically analyze blockchain impacts on CQM from a governance perspective and then investigate the owner and the main contractors' strategic behaviors (e.g., adopting blockchains in CQM or not). The agency theory was used as the theoretical lens to examine how blockchain curbs contractors' quality opportunistic behaviors as a governance mechanism. An evolutionary game theory model was developed to simulate adoption decision interactions between the owner and the main contractor. Finally, policy suggestions were highlighted for policymakers. This research could be one of the first studies examining blockchain potential to CQM from an opportunism governance perspective. Such discussions could inspire more discussions on blockchain-based governance and broaden researchers' and practitioners' understandings of blockchain impacts. The proposed policy suggestions from the equilibrium analysis could facilitate blockchain diffusion in the construction industry. [ABSTRACT FROM AUTHOR]

We use transaction cost economics (TCE) to define the "digitization of transaction terms" shift parameter that describes the institutional changes associated with increased digitization in society. We then draw on legal scholarship to analyze how strong smart contracts, which refer to agreements with automatic execution and enforcement that are not reversible by courts, rely on a new level of digitization of transaction terms. Specifically, these contracts may rely on standard digital infrastructures such as blockchain systems that guarantee automatic execution and non-reversibility. Strong smart contracts represent a distinct mode of transaction governance compared to markets, hierarchies, or hybrids. This is because each classic governance mode is distinguished by how ex post adaptation is handled—through public courts, managerial fiat, or both. In contrast, strong smart contracts prevent ex post adaptation altogether. We propose that when strong smart contracts can be fully specified, they may dominate other governance modes based on certain trade-offs. These trade-offs include weighing the benefits of avoiding the holdup problem and lowering contract enforcement costs against the downsides of high ex ante specification costs and the elimination of flexibility to make ex post adjustments in a changing environment. Our discussion elaborates on which institutional conditions can further facilitate this institutional shift. [ABSTRACT FROM AUTHOR]

The conditional proxy broadcast ReEncryption mechanism enables the user to periodically re-encrypt the cipher-text. When a sender generates the key, ReEncryption id, which reuses for the encrypted data or the ciphertext. Two methods that only allow one recipient at a time are proxy ReEncryption and identity-based proxy ReEncryption. If there are multiple receivers, these systems need to be called numerous times, which is a significant drawback. To overcome this problem of a single receiver, proposed a new scheme, blockchain-based broadcast proxy ReEncryption (BBPRE), which allows the system to handle multiple receivers at a time. This BBPRE works similar to the proxy ReEncryption and identity-based proxy ReEncryption but allows multiple receivers and adds the time property. In this broadcast proxy ReEncryption, a sender/user can able to send ciphertext or encrypted to multiple receivers at a time, and all the receivers also have an encryption key provided by the sender/user.The receivers can able to re-encrypt this encrypted data or the ciphertext. It provides security to the user files during sending and sharing through electronic mail. The time stamp authority creates a public key, each period that has the most recent timestamp for every user. [ABSTRACT FROM AUTHOR]

System requirements can occasionally be ill-defined and difficult to define. In this case, graphic representation of the system created using semi-formal modelling is advantageous. A smart power grid, for example, includes numerous important needs and function points. We can portray the system functionalities graphically to better grasp the requirements before moving forward with the formal definition. Though, graphic models like Unified Modeling Language (UML) have ambiguity issues as they work on predefined rules. On the other hand, the formal technique uses the correct-by-construction method to guarantee the system's dependability. The formal technique is based on well-practiced mathematical notation. In this article, a smart grid (SG) system based on blockchain (BC) is formally modelled and verified. The system requirements are validated using the formal modelling technique Event-B. To illustrate the system, we first suggest various graphical notations. After that, the notations are translated into a JSON (JavaScript Object Notation) format. Java executable codes are generated in order to validate the JSON schemas. The graphical model is then translated into Event-B specifications and validated using RODIN, a standard tool support. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing the implementation of blockchain in logistics in the United Arab Emirates (UAE), including security, privacy, end-user benefits, quality control, technology infrastructure, and lack of general awareness. A mixed-methods approach was used, with data collected through survey questionnaires and semi-structured interviews. The research targeted logisticians in the UAE's supply chain department, using random probability sampling and a sample size of 151 for surveys and using purposive sampling for recruiting 10 experts. Using SPSS 24.0, the Pearson Correlation analysis revealed a strong association between blockchain in logistics and IT infrastructure and awareness of blockchain in the UAE logistics sector. Exploratory factor analysis showed a significant difference towards the security of data in the implementation of blockchain in the logistics sector in the UAE. This research suggests that implementing blockchain technology in UAE logistics companies can enhance data security and quality control, influencing managers' decisions positively and recommends prioritizing data security and quality control. The study's geographical context emphasizes its contribution in the research area showing the importance of considering cost, benefits, security, IT infrastructure ownership, and awareness aspects in the adoption of blockchain technology in supply chain management. [ABSTRACT FROM AUTHOR]

Selfish mining is a sneaky way that some people cheat in blockchain networks or distributed digital ledger systems. They do it by mining a block in secret and keeping it hidden. Then, when the secret chain of these miners' are longer than the real one, they show it to everyone, and the blockchain system selects the longest chain as the valid chain. This leads to the network adopting the longest chain as the valid one, resulting in the effort put into mining by other miners becoming futile. By doing this, selfish miners in the blockchain network have a high potential to get more rewards. This behavior goes against the rules of blockchain networks, where everyone is supposed to play by the same rules and have an equal chance of getting rewards. This prejudiced action of selfish miners have motivated us to investigate systematically the existing methods that are being used to address the selfish mining attacks. Therefore, we conducted a SLR (systematic literature review) of 29 papers using the Kitchenham methodology and put that into PRISMA framework. This study aims to investigate methods for detecting and mitigating selfish mining attacks, their limitations, and future directions. [ABSTRACT FROM AUTHOR]

Smart contracts function like specialized computer programs on the blockchain. Many of these contracts are on Ethereum, but sometimes these contracts have problems with security. These problems caused big money losses and made the blockchain less stable. Smart contracts are self-executing with predefined rules and are at the core of many blockchain applications. However, they are susceptible to various vulnerabilities and security risks. Automated vulnerability detection helps identify and mitigate these issues efficiently. Smart Contracts (SC) have become really popular lately. People think they are the future for making deals on blockchains. Smart contracts are like automatic agreements. They work by themselves using special computer programs. They follow the rules of the deal and keep track of everything. The main idea with smart contracts is to get rid of the need for traditional trusted middlemen-like authorities or organizations. Instead, we use code that runs on a secure and unchangeable system. In this manuscript, pioneering automated vulnerability detection for smart contracts in blockchain using KEVM: Guardian ADRGAN (ADRGAN-SCB-KEVM) is proposed. Here, K framework's Ethereum virtual machine (KEVM) is a computation engine used in this research. From this KEVM, smart contracts data are provided to feature extraction phase. Feature extraction is done using Nested patch-based feature extraction. Then the extracted features are fed to attentive dual residual generative adversarial network (ADRGAN), identifying KEVM smart contracts vulnerabilities. Finally, by using ADRGAN, it is classified as Vulnerable and Non-Vulnerable in smart contracts. The proposed ADRGAN-SCB-KEVM method employed on Python and efficiency of proposed method evaluated with different metrics like Accuracy, Computation Time, Precision, Recall, F1 Score, Specificity, RoC are evaluated. The simulation outcomes prove that the proposed ADRGAN-SCB-KEVM technique attains 41.34%, 31.28%, and 36.38% higher Accuracy for Vulnerable; 32.44%, 38.45%, and 29.47% higher Accuracy for Non-Vulnerable while compared with the existing methods such as Utilizing fault injection to evaluate blockchain systems in the presence of faulty smart contracts (UFI-BS-ESVD), State-of-the-Art Blockchain-Enabled Smart Contract Applications in the University (SA-BESC-ESVD), enhancing Ethereum smart contracts static analysis by computing precise Control-Flow Graph of Ethereum bytecode (EESC-CPC-EBD), respectively. [ABSTRACT FROM AUTHOR]

A MANET is a decentralized type of wireless network of mobile devices, and it can also be defined as an autonomous system of nodes. All the nodes in the network are connected by wireless links and are mobile. They can come together and form a network without any support from any existing network infrastructure. MANET is a new field of study based on blockchain in a wireless ad hoc environment. However, the main challenge for blockchain applications in ad hoc networks is how to adapt to the extreme computational complexity of block validation while preserving the characteristics of the blockchain and including nodes in the validation process. This article proposes a blockchain-based mobile network (MANET) with an ensemble algorithm. The proposed scheme provides a distributed environment for MANETS routing using a blockchain based on the Byzantine fault tolerance (BFT) protocol. Taking advantage of the better approach of mobile ad hoc networking (BATMAN) to incorporate the concept of blockchain into the MANET as a representative protocol. The proposed method named Extended-BATMAN (E-BATMAN) incorporates the concept of blockchain into the BATMAN protocol using MANET. As a secure, distributed, and reliable platform, Blockchain solves most BFT security issues, with each node performing repeated security operations individually. The experimental analysis of the proposed ensemble algorithm is based on four parameters such as packet delivery rate, average end-to-end latency, network throughput, and energy. All of these parameters show better results with the proposed ensemble protocol than with existing state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Cybersecurity is an essential part of IoT device functionality. Malicious acts cause the disclosure of private data, putting device performance at risk. As a result, creating efficient security solutions for authentication and confidentiality of both IoT devices and data exchange and integrity of data exchange networks has become a significant challenge. Furthermore, the traditional security mechanisms' high computation demands are not suitable for resource-constrained specific IoT devices. Therefore, we have proposed a security system for IoT applications using blockchain called BLISS, which ensures robust identification, authentication, confidentiality, and integrity of IoT devices and data exchange. Using smart contracts, the BLISS creates trustful clusters of IoT devices through an authentication process for data exchange. The BLISS is implemented on the Raspberry Pi 4 and the desktop PC, considering the Raspberry Pi 4 as an IoT device and the desktop as a cluster head. The performance analysis of the BLISS demonstrates the enhanced performance in the context of computation and energy consumption, which is 62–65% reduced. The storage and communication overhead is reduced by up to 70% compared with state-of-the-art schemes. The security analysis showed that the proposed scheme withstands many IoT-specific cyber threads. [ABSTRACT FROM AUTHOR]

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

The widespread growth of the Internet of Things (IoT) in various application domains makes device security and data privacy a critical concern. Indeed, the continuous discovery and exchange of resources between IoT devices through central Resource Directories (RD), running on standardized application-layer protocols, such as Constrained Application Protocol (CoAP) causes trust and reliability issues. Blockchain (BC) offers a promising reliable and non-repudiable solution through consensus and decentralization. Nevertheless, its applicability in IoT environments is still challenging. This paper proposes DRDChain: a lightweight permissioned blockchain framework for discovering and consuming IoT resources. DRDChain deploys Distributed RDs (DRDs) as validating peers and constrained IoT devices as BC clients. Resource registration and discovery operations in DRDChain are modeled as smart contracts and support multiple data storage approaches. A prototype of DRDChain is implemented using Hyperledger Iroha and CoAPthon. Extensive evaluations in a realistic IoT environment have demonstrated the effectiveness and efficiency of DRDChain in terms of reliability, time, throughput, and storage size. [ABSTRACT FROM AUTHOR]

In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate. [ABSTRACT FROM AUTHOR]

Blockchain technology has been increasingly integrated into various fields of economic and social development. However, scalability issues such as low broadcast efficiency, high communication overhead, and high redundant transmission rate have greatly limited the large-scale application of blockchain. This paper proposes a new scalability-enhanced Kademlia network protocol (SEKad), which optimizes blockchain network transmission in terms of both network topology and communication broadcast mechanism, provides a new approach to solving the scalability problem from the network layer. In SEKad protocol, firstly, SEKad employs a method that aligns the physical topology with the network topology for node clustering. This strategy groups neighboring nodes into clusters, streamlining network complexity and reducing network diameter. Secondly, SEKad presents a cluster-adaptable network topology based on Kademlia (Kad-CA). Kad-CA improves the rationality and efficiency of per-hop routing, optimizing the construction of K buckets and thus enhancing overall network efficiency. Finally, within the network transmission model, SEKad devise the smallest sub-tree broadcasting mechanism and the minimum redundant transmission mechanism for Kad-CA. These mechanisms work to reduce the redundant transmission rate, and enhance broadcasting efficiency. In order to evaluate the performance of SEKad, we built an experimental platform and conducted evaluations. The experimental results show that SEKad has better network scalability and stability, the network diameter size can be reduced by 27% and the redundant transmission rate can be reduced by 30%. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

Human microarray gene profile analysis has become popular for early cancer detection. Due to security risks, Healthcare 4.0 standards make processing patient gene data difficult. Healthcare 4.0 guidelines must secure gene profile data storage and exchange. Recently several blockchain-based methods have been proposed for secure data processing for Cloud-based Healthcare 4.0 architecture. However, existing solutions not providing all the essential features like scalability, strong security measures, computational efficiency, and multiple security parameters. To overcome these problems, we propose a novel approach for the secure processing of the microarray gene expression data using computationally efficient, scalable, and highly secured Healthcare 4.0 standards. We propose a new Healthcare 4.0 architecture based on lightweight cryptography and blockchain technologies. Blockchain technology's recent advancements in cloud data storage and sharing indicate potential security features. Smart healthcare applications need a centralized approach for safe data storage and exchange. Edge layer (patients/gene users), fog, cloud storage, and blockchain make up Healthcare 4.0. Fog nodes store and search edge-layer gene data in blockchain-enabled cloud storage using lightweight cryptography. Elliptic Curve Cryptography (ECC) protects data processing and privacy using Elliptic Curve Diffie–Hellman (ECDH) and Elliptic Curve Digital Signature Algorithm (ECDSA). Meta-data from cloud-saved data is stored in blockchain to prevent tampering, forging, and quantum. We designed different threat-based case studies to demonstrate the model's efficiency and scalability. Computing efficiency and security are better with the proposed strategy. The outcome case studies showed that the suggested approach protected against various opponent models. The proposed method reduces encryption and decryption times by 325 and 20 ms, respectively. [ABSTRACT FROM AUTHOR]

Electronic medical records have the advantages of large storage capacity, convenience, improved efficiency, etc. However, since electronic medical records contain a large amount of patient's private information, if they are stored directly in the cloud server, patients may face the risk of privacy data leakage and electronic medical record data tampering. To solve the above problems, this paper proposes a secure sharing scheme of electronic medical records combining quantum key and blockchain. Based on the true randomness of quantum keys, the multi-user secure forwarding mechanism of quantum keys is designed using proxy re-encryption technology and blockchain. A fine-grained access control scheme for electronic medical records by users with different roles was proposed using data desensitization technology. To facilitate secure retrieval from the cloud server, searchable encryption technology is used. This paper analyzes the comprehensive performance level of the proposed scheme through blockchain performance testing and system efficiency testing. [ABSTRACT FROM AUTHOR]

In today's technology landscape, the Internet of Things (IoT) has gained significant momentum due to its wide-ranging applications across diverse devices. However, the integration of IoT faces several daunting challenges, with security emerging as a paramount concern. To address this, blockchain technology has emerged as a decentralized and foundational solution for mitigating these integration obstacles. This research introduces a reliable blockchain-based consensus PIoT (Proof of Internet of Things) specifically for IoT networks, aiming to overcome the attacks encountered during data communication. The proposed blockchain system excels in effectively identifying IoT devices, authenticating their legitimacy, and dynamically assigning addresses based on demand. Additionally, it offers secure communication capabilities among IoT devices, providing robust protection against various attacks. The performance of PIoT is superior than that of certain traditional techniques. As it comes to the efficient use of available time, PIoT displays improvements of 74.7 and 96.8%, respectively, as compared to LPBFT and hybrid blockchain. In addition to this, the level of consensus security offered by PIoT is 15% higher than that offered by DAC4SH. PIoT has impressive scalability in terms of time consumption, the fraction of successful consensus attempts, and transaction throughput even when the network size and the number of malicious nodes face exponential variations. [ABSTRACT FROM AUTHOR]

A blockchain is a distributed general ledger that, after authentication, records cryptographic transactions. Consensus algorithms are used to record transactions in the general ledger. Consensus algorithms are designed to achieve reliability in a network involving untrusted nodes. Although several protocols have been in operation for several years, they still have drawbacks. Artificial intelligence (AI) and blockchain are two technologies that have received a lot of attention in the last decade and are rapidly growing. Fascinating results can be obtained by applying AI to solve existing blockchain challenges. Cognitive blockchain employs artificial intelligence to improve performance and solve its challenges. Cognitive blockchain can perceive network conditions, analyze gained knowledge, effectively decide, and adapt to improve network performance. CGG, which models distributed systems with many simple objects that interact with each other locally, could be a suitable learning model for the cognitive blockchain because of the distributed nature of the blockchain network. We presented a new consensus protocol based on the Cellular Goore Game (CGG) as a cognitive model for blockchain consensus that could be used as intelligence consensus in the cognitive blockchain. The proposed consensus protocol partitions the nodes that take part in the consensus, organizes the CGG model to verify a new block after publication, and finally, based on the CGG's result, confirms or rejects the block. A new intelligent consensus approach improves fault tolerance, scalability, and performance and reduces participation costs in consensus for private and consortium blockchains. We conduct a qualitative study of the proposed protocol and analyze its experimental performance. The results show that the proposed schema performs well in the presence of numerous faulty nodes, as well as in terms of scalability and throughput. [ABSTRACT FROM AUTHOR]

The convergence with the emerging digital twin technology has been envisaged as the looming of a smart cloud-based healthcare system. Precision clone simulation of the patients and medical service builds a bridge to the virtual medical world, enabling diagnosing, monitoring, and future prediction to offer enhanced medical services. Cloud services provider plays a crucial role in endowing a way to interlink the physical world to the virtual medical world through outsourced storage, a communication platform, reduced computational cost, and the relevance of hosting and other services over the internet offered by it. However, any alteration, deviation, or amendment of the physical patient's and its digital twin's stored data in the cloud storage has been one of the security liabilities. In this work, we propose a blockchain-assisted certificateless public auditing mechanism for the cloud-based digital twin healthcare networks to substantiate the data integrity in cloud storage. Blockchain in the proposed mechanism enables keeping the auditing process and outsourcing activities log. Meanwhile, it also enjoys the benefits of batch auditing and pairing-free operations. The proposed work is demonstrated to be provably secure and efficient in achieving security goals like storage correctness, privacy-preserving, and public verifiability. Furthermore, the computational overhead of the presented work is shown to be efficient with contrasted with some existing works. [ABSTRACT FROM AUTHOR]

Blockchain is a modern-age technology which records digital transactions in a consistent, safe, and verifiable form to solve the problems from different sectors. In the supply chain, this technology is applied to improve traceability, ownership, and trust. Moreover, it can solve the issue pertaining to counterfeits in the supply chain. However, blockchain-based supply chains have certain disadvantages, such as privacy of the information shared between the members of the supply chain. In this paper, a new mechanism has been proposed to ensure the privacy of such information by taking traceability and ownership into account. The novelty of the proposed system is its ability to achieve privacy with the inclusion of traceability and ownership. To implement privacy, we employed a combination of symmetric and asymmetric key encryption. With the proposed system, owner of the product can trace said product and the one who owns the product can transfer the ownership. The proposed system restricts the entry of counterfeit products in the supply chain. The Hyperledger Sawtooth blockchain has been used for the experimental purpose. The subsequent analysis show that the proposed system can provide privacy without disturbing factors concerning traceability and ownership. The results show that the inclusion of privacy does not impact performance significantly. [ABSTRACT FROM AUTHOR]

Over the last half-century, consumer research has often depicted scarcity as a dominant factor increasing price. But should we assume that scarcity's upward pressure on price remains intact, in a world where novel forms of digital products proliferate? In this article, we propose that blockchain-encrypted digital goods, in particular, non-fungible tokens (NFTs), offer good reason to revisit this assumption. In this context, we argue and find that social value can outweigh intrinsic value as a determinant of willingness-to-pay. As a result, when scarcity threatens access to high levels of social value, its effect on price can be negative rather than positive—an inversion of a pattern typically observed for offline collectibles. Secondary data taken from the NFT platform Opensea and a set of experimental studies support this social value-based lens. Given these findings, we propose a research agenda to ground future work in this area. We also suggest that NFTs offer a laboratory in which past theories related to social value, scarcity, and price can be reconsidered and future theories developed, hopefully allowing consumer researchers to lead knowledge development in these domains over the next 50 years. [ABSTRACT FROM AUTHOR]

This research shows that although the used and unused versions of a digital good (e.g. virtual apparel) are identical in every pixel and functionality, consumers tend to prefer the unused version. This "genesis effect" occurs because consumers tend to perceive used (vs. unused) digital goods as virtually contaminated and because being permanently listed as the first (vs. subsequent) owner in the ownership record can confer a greater sense of status. Specifically, in study 1, analyses of large-scale field data on purchases of digital goods in the metaverse showed that consumers paid substantially more to acquire the unused (vs. used) version of the same good. Studies 2–4 causally demonstrated the genesis effect and its underlying mechanism across metaverse product categories—participants were less likely to purchase digital goods described as used (vs. unused). Virtual contamination and virtual status jointly mediated the effect. Furthermore, being the first—at the genesis of a digital product's usage history—was particularly special, such that participants were less sensitive to increases in the number of prior owners after the first one. Finally, showing participants that a used good had been digitally reconstituted attenuated the genesis effect. These findings add to the literature on consumer behavior in the metaverse and offer managerial insights on digital goods marketing. [ABSTRACT FROM AUTHOR]

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput. [ABSTRACT FROM AUTHOR]

Recently, Metaverse is gaining prominence within the field of radiology due to its potential to revolutionize image visualization. Radiologists can harness its capabilities to access dynamic, highly detailed results, thereby enhancing diagnostic precision. Digital twins, at the core of the Metaverse, are digital replicas of real-world objects and entities. They serve as the foundational building blocks, enabling the creation of virtual counterparts for everything within the Metaverse. To ensure the reliability of these digital twins, blockchain technology offers a multi-dimensional data storage solution, reinforcing data integrity and trustworthiness. It is used to ensure a transparent and 3D visualization of each communication and interaction for further looking up any criticality if present in the network. With the rapid increase in value and volume of data, the evolution of the metaverse faces a number of privacy and security concerns. Furthermore, the metaverse in Industry 4.0 is a trending topic that further needs to focus on its security challenges at its initial stage. Fortunately, blockchain is considered as one of the significant solutions. The aim of this paper is to propose a secure and efficient fuzzy evaluation matrix by computing the trust values along with integrating with blockchain mechanism in industry 4.0 enabling metaverse environment. The proposed mechanism is validated against various security concerns such as broken authentication, eavesdropping, personal information leakage, data injection, and unauthorized access. The proposed mechanism showed the validation rate against existing schemes with an improvement of 94% in comparison of several security metrics. [ABSTRACT FROM AUTHOR]

Private set intersection (PSI) enables two parties to determine the intersection of their respective datasets without revealing any information beyond the intersection itself. This paper particularly focuses on the scenario of unbalanced PSI, where the sizes of datasets possessed by the parties can significantly differ. Current protocols for unbalanced PSI under the malicious security model exhibit low efficiency, rendering them impractical in real-world applications. By contrast, most efficient unbalanced PSI protocols fail to guarantee the correctness of the intersection against a malicious server and cannot even ensure the client's privacy. The present study proposes a blockchain-based unbalanced PSI protocol with public verification and financial security that enables the client to detect malicious behavior from the server (if any) and then generate an irrefutable and publicly verifiable proof without compromising its secret. The proof can be verified through smart contracts, and some economic incentive and penalty measures are executed automatically to achieve financial security. Furthermore, we implement the proposed protocol, and experimental results demonstrate that our scheme exhibits low online communication complexity and computational overhead for the client. At the same time, the size of the generated proof and its verification complexity are both O (l o g n) , enabling cost-effective validation on the blockchain. [ABSTRACT FROM AUTHOR]

Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods. [ABSTRACT FROM AUTHOR]

E-commerce platforms incorporate reputation systems that allow buyers to rate sellers after transactions. However, existing reputation systems face challenges such as privacy leakage, linkability, and multiple rating attacks. The feedback data can inadvertently expose user information privacy because they reveal the buyers' identities and preferences, which deters a significant number of users from providing their ratings. Moreover, malicious actors can exploit data analysis and machine learning techniques to mine user privacy from the rating data, posing serious threats to user security and trust. This study introduces ARS-Chain, a pioneering and secure blockchain-driven anonymous reputation-sharing framework tailored for e-commerce platforms. The core of ARS-Chain is a dynamic ring addition mechanism with linkable ring signatures (LRS), where the number of LRS rings is dynamically added in alignment with the evolving purchase list, and LRS link tags are constructed with the LRS rings and item identifiers. Further, a consortium blockchain is introduced to store these anonymous ratings on e-commerce platforms. As a result, ARS-Chain ensures full anonymity while achieving cross-platform reputation sharing, making rating records unlinkable, and effectively countering multiple rating attacks. The experimental results confirm that ARS-Chain significantly enhances user information privacy protection while maintaining system performance, having an important impact on the construction of trust mechanisms for e-commerce platforms. [ABSTRACT FROM AUTHOR]

As the usage of electric vehicles (EVs) expands, various energy management technologies, including battery energy storage systems, are being developed to efficiently charge EVs using various energy sources. In recent years, many blockchain-based energy trading schemes have been proposed for secure energy trading. However, existing schemes cannot fully solve privacy issues and security problems during energy trading. In this paper, we propose a reliable and privacy-preserving vehicular energy trading scheme utilizing decentralized identifier technology. In the proposed scheme, identity information and trading result information are not revealed publicly; this is due to the use of decentralized identifiers and verifiable credential technologies. Additionally, only parties who have successfully conducted energy trading can manage complete transaction information. We also demonstrate our method's security and ensure privacy preservation by performing informal and formal security analyses. Furthermore, we analyze the performance and security features of the proposed scheme and related works and show that the proposed scheme has competitive performance. [ABSTRACT FROM AUTHOR]

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024. [ABSTRACT FROM AUTHOR]

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks. [ABSTRACT FROM AUTHOR]

The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model's robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples. [ABSTRACT FROM AUTHOR]

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures. [ABSTRACT FROM AUTHOR]

Summary: The discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s. [ABSTRACT FROM AUTHOR]

In recent years, cloud manufacturing has brought both opportunities and challenges to the manufacturing industry. Cloud manufacturing enables global manufacturing resources to be unified and shared, thus breaking down geographical constraints to enhance the level and efficiency of manufacturing. However, with the explosive growth of manufacturing resources and user demands, traditional cloud manufacturing platforms will face problems of insufficient computility, lack of real‐time data and difficulties in securing user privacy during the service selection process. In this article, a blockchain‐based decentralized cloud manufacturing service selection method is proposed, where the computility resource is deployed in multiple distributed nodes rather than the traditional centralized cloud manufacturing platform to solve the problem of insufficient computility. The credibility of the users is evaluated based on their performance on the contract and the PBFT consensus algorithm is improved based on the credibility of the users. In addition, a tri‐chain blockchain data storage model is designed to ensure the security, real‐time and transparency of data in the cloud manufacturing service selection process. The experimental results show that the method both speeds up service selection process and improves the quality of service selection results, and achieves a significant increase in manufacturing efficiency. [ABSTRACT FROM AUTHOR]

In traditional contexts, enterprises often hesitate to engage in data sharing due to the associated heightened risks and transaction costs. Blockchain technology, leveraging its encryption algorithms and smart contracts, effectively alleviates these challenges, thereby encouraging enterprises to actively participate in data sharing. The adoption of reasonable reward and penalty mechanisms can prevent opportunistic behaviours and stimulate data sharing among businesses. It is noteworthy that blockchain technology and data alliance have a mutually substitutive effect in terms of risk and communication costs, while they complement each other in regulatory and transaction costs. The integration of blockchain technology with data alliance can enhance collaborative revenue and reduce various costs. When distributing collaborative revenue, it is crucial to adhere to the guiding principle of ‘prioritizing efficiency while considering fairness’. Collaboration becomes a reality when all parties collectively achieve a substantial overall revenue threshold. Subsequently, in-depth exploration of fair resource allocation principles is crucial to ensure the equitable distribution of resources. [ABSTRACT FROM AUTHOR]

The healthcare systems follow the path of client-server architecture in a centralized manner for managing the patient's health information with the secure storage process. Medical data can be preserved in every medical institution that remains safely in silos. It could be able to access without proper authorization and cannot be transferred. At the same time, while handling massive data, the clinics or hospitals are fragile enough to govern the patient's health information as the system is confined with such security constraints. Rather than other methodologies, blockchain plays a pivotal part in the medical industry for storing medical data in a secure manner. Since the blockchain represents a reliable and scalable decentralized network, it can conquer certain challenges of classical methods. Furthermore, the Internet of Things (IoT) is an emerging process of fetching data by wearing some sensors and devices. Through these intelligent elements, the healthcare data can be shared and enriched with the data quality for effective healthcare services. Nevertheless, this connection becomes vulnerable to data privacy and security because the data management relies on open service networks. In order to overcome these effects, a novel IoT-derived blockchain model is proposed for storing and authenticating purposes. Initially, medical data are collected from different benchmark sources and subjected to the data encryption phase. Then, encryption is performed with the help of Adaptive Elliptical Curve Cryptography and Rivest-Shamir-Adleman (A-ECC-RSA), where the key optimization is done by utilizing Enhanced Sand Cat Swarm Optimization (ESCSO). Further, encrypted data are stored in the blockchain and finally authentication access is performed with the help of Adaptive Bidirectional Long Short-Term Memory (ABiLSTM) using biometric information, where the BiLSTM parameters are optimally selected by developed ESCSO. Thus, the developed model will attain an effective security rate than conventional models that can be established through several experiments on the proposed model. [ABSTRACT FROM AUTHOR]

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental & Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios. [ABSTRACT FROM AUTHOR]

• Propose a data-driven Chatbot framework based on Blockchain technology to manage maintenance activities via building information modeling (BIM) technology. • Assist government authorities and asset managers adopt building information modeling (BIM) technology to preserve heritage buildings. • Mimic human interaction via text communication with respect to maintenance activities via an intelligent interactive Chatbot system. Preservation of cultural heritage in the face of human and natural disasters, as well as improper maintenance practices, is a crucial endeavor that must be undertaken throughout the lifespan of heritage buildings. In order to maximize the value derived from these assets and strike a harmonious balance between accurate documentation and performance optimization, asset management strategies have been employed. In recent years, the advent of innovative methodologies such as Building Information Modeling (BIM) and laser scanning has opened new possibilities for their application in various contexts. However, successful implementation of these methodologies faces a range of challenges, including limited knowledge, inexperienced teams, and reluctance to embrace change on the part of administrators. To address these obstacles and enhance asset management practices, the integration of BIM, Chatbot, and Blockchain methodology has emerged as a viable solution. This methodology not only facilitates the improvement of data security and user data capture but also streamlines the process of updating BIM models. In this research, we propose a data-driven Chatbot framework based on Blockchain, specifically designed to manage maintenance activities through the utilization of BIM. The proposed framework serves as a valuable tool for government authorities and asset managers seeking to adopt BIM for the preservation of heritage buildings. Comprising three key components – the BIM model, the Blockchain model, and the Chatbot model – the framework offers comprehensive support. The BIM model encompasses all relevant information pertaining to the management of the heritage building, while the Chatbot model replicates human interaction through text-based communication. Users can access up-to-date BIM data by posing predefined questions to the Chatbot. On the other hand, the Blockchain model serves as a secure and immutable record-keeping system, validating and verifying user updates, thereby fostering trust among stakeholders. This proposed model empowers asset stakeholders to request and update asset information via a trusted and reliable channel. To showcase the capabilities of the framework, we present a real-world case study centered on the Omar Toson Palace in Cairo, Egypt. Through this case study, we demonstrate the effectiveness and potential of our proposed framework in meeting the challenges associated with heritage building preservation. [ABSTRACT FROM AUTHOR]

To address the concerns about security and privacy in cloud-based data storage, this study proposes a unique approach to secure cloud-based data governance using hybrid machine learning architectures. With the increasing reliance on cloud resources and the need for improved security measures, this research aims to provide a solution that enhances data access governance while ensuring data privacy. To achieve this, the study introduces a Distributed Data Service (DDS) specifically designed for collecting and analyzing data in Internet of Things (IoT) contexts. The DDS enables various IoT middleware methods to exchange common data services from a loosely coupled provider. The functional specifications of the DDS are revised to facilitate effective data gathering, filtering, and storage in this environment. The proposed approach leverages distributed cloud architecture and hybrid machine learning techniques to establish cloud data access governance with enhanced security. A privacy-aware architecture is built upon distributed middleware services and supply chain quantum neural networks to ensure data privacy and protection. Additionally, multi-agent federated Bayes vector-based blockchain and partial homomorphic encryption techniques are employed to enhance network security. Throughout the experimental investigation, several performance metrics are considered. Scalability, latency, quality of service, data integrity, and time consumption are analyzed to assess the efficiency of the proposed approach. By addressing security and privacy concerns in cloud-based data storage through the utilization of hybrid machine learning architectures and distributed cloud infrastructure, this research provides a novel approach to cloud data access governance. Overall, the study offers a comprehensive solution to secure and privacy-aware data management in the cloud, paving the way for further advancements in this field. [ABSTRACT FROM AUTHOR]

The presence of the malicious internet of things (IoT) devices in wireless sensor networks (WSNs), in the underlying work, is identified using a private blockchain and interplanetary file system (IPFS) based system. In the proposed model, the registration of IoT devices is being done via the IoT device manager. After registration, the IoT devices send a request to get services from the IoT device manager. At this stage, authentication is performed through a machine learning technique, known as logitboost (LB), which helps in identifying the IoT devices as malicious or legitimate. A verification report is forwarded to the IoT device manager upon detection. If the IoT device is found to be acting maliciously, it is penalized and vice versa. Thus, ensuring the IoT device manager provides the required service only to the legitimate IoT device. The data used in the proposed work is taken from the WSN dataset (WSN-DS), which is balanced via the synthetic minority oversampling technique. Additionally, the miner nodes' verification is performed via verifiable byzantine fault tolerance (VBFT). Also, IPFS is used to store the transaction data being shared between the IoT devices. The simulation results show that VBFT surpasses the proof of authority consensus mechanism by 15–20% in terms of transaction throughput. Furthermore, the LB classifier is found to surpass decision tree, nearest centroid, quadratic discriminant analysis and weighted average ensemble by 2%, 24%, 71%, 5% in terms of accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present a new encryption method based on discrete wavelet transform (DWT). This method provides a number of advantages as a pseudo randomness and sensitivity due to the variation of the initial values. We start by decomposing the image with spatial reconstruction by DWT, followed by preformation by fractional chaotic cryptovirology and Henon map keys for space encryption. Bearing in mind the rapid pace of change in the fields of fractional chaotic cryptovirology, our findings suggest that the proposed method is too powerful relative to the key sensitivity, histogram, entropy and correlation coefficient. [ABSTRACT FROM AUTHOR]

With the increasing demand for decentralized systems and the widespread usage of blockchain, low throughput and high latency have become the biggest stumbling blocks in the development of blockchain systems. This problem seriously hinders the expansion of blockchain and its application in production. Most existing smart contract scheduling solutions use static feature analysis to prevent contract conflicts during parallel execution. However, the conflicts between transactions are complex; static feature analysis is not accurate enough. In this paper, we first build the dependency between smart contracts by analyzing the features. After numerous experiments, we propose a conflict model to adjust the relationship between threads and conflict to achieve high throughput and low latency. Based on these works, we propose adaptive parallel scheduling for smart contracts on the blockchain. Our adaptive parallel scheduling can distinguish conflicts between smart contracts and dynamically adjust the execution strategy of smart contracts based on the conflict factors we define. We implement our scheme on ChainMaker, one of the most popular open-source permissioned blockchains, and build experiments to verify our solution. Regarding latency, our solution demonstrates remarkable efficiency compared with the fully parallel scheme, particularly in high-conflict transaction scenarios, where our solution achieves latency levels just one-twentieth of the fully parallel scheme. Regarding throughput, our solution significantly outperforms the fully parallel scheme, achieving 30 times higher throughput in high-conflict transaction scenarios. These results highlight the superior performance and effectiveness of our solution in addressing latency and throughput challenges, particularly in environments with high transaction conflicts. [ABSTRACT FROM AUTHOR]

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts. [ABSTRACT FROM AUTHOR]

Access control systems have become integral to every organization's defensive arsenal to protect sensitive information and ensure that businesses comply with data privacy regulations. The goal is to block out anyone who shouldn't be able to get their hands on sensitive information. Authorization solutions like access control help keep sensitive information safe by enforcing strict rules against unauthorized users. This study investigates the difficulties of pushback request response management in P2P electronic health records (EHRs) and suggests a blockchain-enabled EHR for a decentralized P2P healthcare facility. To prevent privacy breaches in the blockchain network and to make information more easily accessible, a new method of authentic access control has been created and implemented. The PRR is an innovative method proposed where security-based mathematical representation and modelling on EHR datasets create a proven security aspect of data privacy. We tested on-chain and off-chain computing modalities by implementing them. Simulations with our implementation show that our suggested pushback request response to medical data significantly lowers the overall peak request. The off-chain computing method offers lower delay time and overhead than the on-chain mode while maintaining the same level of system integrity. In addition, we evaluated the suggested method in terms of its performance and security, and we compared it to an existing hybrid method while taking into account data from the New York State Department of Health. [ABSTRACT FROM AUTHOR]

There are issues such as dishonest checking organizations and unreliable data storage in a deduplication scheme based on data popularity. This paper proposed a data popularity deduplication model for decentralized storage. To address the dishonesty of the checking agency, the model executed duplication checking and popularity checking of data by using smart contracts as the checking organization, which combined the non-tamper-ability of blockchain with the non-repudiation of smart contracts. It guaranteed the authenticity of the test results. To address the problem of unreliable data storage, it proposed a file chain storage structure. This structure satisfied the requirement of data popularity deduplication. Meanwhile, this structure established the logical relationship between the fragments which were distributed in different storage nodes to achieve physical/logical uploads by adding auxiliary information. The file chain storage structure provided the basis for popularity data in decentralized network storage. Besides, it added backup identifiers to the data block information, and divided the storage network into two virtual storage spaces with the help of the backup identifiers. Also, two virtual storage spaces were oriented for data detection and storage. It satisfied the user’s backup needs. Security analysis and performance analysis show that the scheme is feasible, guarantees the authenticity of the test results, and improves the reliability of data storage. [ABSTRACT FROM AUTHOR]

This paper proposed a dual-layer consensus algorithm (DL_RBFT) based on the enhanced Raft and practical Byzantine fault tolerance (PBFT) consensus algorithms to address scalability issues, excessive communication overhead, and challenges in large-scale network node environments in use within consortium blockchains. Initially, it divided blockchain nodes into several groups to form a lower-layer consensus network. Then, leaders from these groups constituted an upper-layer consensus network, creating a dual-layer consensus structure. In the lower-layer consensus network, it improved Raft consensus algorithm by introducing supervision and reputation mechanisms, while the leader election process introduced ant colony optimization to maintain high efficiency. In the upper-layer consensus network, it utilized the PBFT consensus algorithm for consensus. The enhanced Raft consensus algorithm exhibited resistance to Byzantine node attacks, enhancing the algorithm’s security. Experimental results indicate that compared to traditional PBFT consensus algorithms, DL_RBFT reduces consensus latency by two orders of magnitude and improves throughput by one order of magnitude in a 100-node scenario. In comparison to other enhanced algorithms, DL_RBFT demonstrates significant advantages. Therefore, DL_RBFT consensus algorithm exhibits strong scalability and can be widely applied in various consortium blockchain scenarios. [ABSTRACT FROM AUTHOR]