Muhammad Jawad, Mahmood A. Al-Shareeda, Omar Yawez Mustafa Mustafa, Mohammed Amin Almaiah · 5 authors
Analysis of repeated attack signatures is important because of the rapid evolution of the Social Internet of Vehicles (SIoV). However, threats such as replay attacks, session hijacking, and key reuse make secure communication between vehicles, roadside units (RSUs), and the fog node difficult. Traditional models for authentication are limited by computational overhead and lack quick key revocation. In response to these challenges, we propose a hybrid cryptographic authentication scheme that combines a Zero-Knowledge Proof (ZKP) with AES-GCM encryption. Our protocol implements a dynamic key revocation mechanism to avoid rogue and session key migration, minimizing re-authentication delay. Security analysis in the Real-Oracle Random (ROR) model shows that it is not vulnerable to impersonation or replay attacks. Evaluations demonstrate decreases of 58% in authentication latency while achieving 45% and 72% improvements in communication and computation efficiency, respectively. Our approach is also scalable and secure, providing SIoV with higher reliability for automotive applications in the vehicular networks of the future.
Sergii Grybniak, Yevhen Leonchyk, Igor Y. Mazurok, Oleksandr S. Nashyvan · 6 authors
This paper presents an approach to improving the scalability of the decentralized smart contract platform Waterfall, based on the concept of hierarchical fractal sharding. Although distributed ledger technology holds significant promise for building secure and transparent digital ecosystems, its widespread adoption remains limited by scalability issues. A key challenge lies in the inability to proportionally increase transaction throughput with the growing number of participants without undermining either decentralization or security. The proposed solution reduces both computational and communication loads by distributing transactions, smart contracts, and network state across a system of recursively structured shards. Each shard operates as an independently validated subnetwork organized as a directed acyclic graph structure that supports asynchronous execution and consensus. This design enables the participation of low-power nodes, enhances load balancing, and achieves scalability not only at the level of the entire network but also within its internal components. The study details the mechanisms for shard formation and merging, transaction routing strategies, and dynamic placement of smart contracts. In addition, a probabilistic model is introduced to evaluate the risk of malicious capture of individual shards, and guidelines are provided for choosing safe shard sizes under various threat assumptions. While the proposed architecture is designed specifically for the Waterfall platform, its core principles and several of its methods may be adapted to other distributed ledger systems, including but not limited to blockchain-based platforms, particularly those employing modular or directed acyclic graph-structured architectures.
Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.
Open access
Artificial Intelligence in Healthcare and Education
An E-voting framework utilizing decentralized technology can establish a secure and transparent environment for elections, where voters can confidently cast their ballots knowing that their votes are final and untampered with. Blockchain's decentralized structure ensures that votes are recorded accurately, preventing interference from external actors. In a protected Evoting framework, each vote becomes part of an immutable, distributed ledger, allowing for peer-to-peer validation of transactions. This ensures that each voice counts as the only, unchanging record. The results can be reported immediately as soon as the voting process is completed. Voting is a critical process carried out in democratic societies, usually through secret voting documents or other similar methods. However, traditional voting systems are often plagued by problems such as voting manipulation, low turnout and logistics challenge. To solve these problems, we propose implementation of decentralized voting platforms that offer advanced security, efficiency and confidence in the election process
Open access
2 source records
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The demand for halal food products is increasing rapidly around the world. The consumption of halal food products is just not among Muslims but also non-Muslims, due to the purity of the halal food products. However, there are several challenges that are faced by halal food consumers. The challenges raise doubt among the halal food consumers about the authenticity of the product being halal. Therefore, a solution that can address these issues and establish trust between consumers and producers is needed. Blockchain technology can provide a distributed ledger with an immutable record of the information. Artificial intelligence supports developing a solution for pattern identification. The proposed research utilizes blockchain an artificial intelligence-based system for developing and e-adoption of a system that ensures the authenticity of halal food products by providing traceability related to all the operations and processes of the supply chain and sourcing the raw material. The proposed system has been tested with a local supermarket in a sandbox environment. The results and tests of the developed solution seemed effective and the testers expressed interest in real-world implementation of the proposed system. Therefore, the findings contribute to the knowledge of technology adoption by examining the application of the proposed system in halal food supply chain and provide insight into the potential benefits for e-adoption in this domain.
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
Multiple industries bear great potential from blockchain technology although scalability issues create barriers to mass implementation. This paper investigates the basic scalability difficulties of blockchain systems while analyzing the trade-offs from the blockchain trilemma which includes decentralization, security and scalability. We conduct a discussion about consensus procedures which determine scalability strength and we evaluate Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT). This paper evaluates both on-chain and off-chain solutions such as block size enlargement along with Segregated Witness (SegWit) and sharding and payment channels and sidechains. We study Layer 1 and Layer 2 scaling solutions to deliver extensive information regarding blockchain scalability improvement methods which create paths toward future technological advancement.
Following the emergence of the COVID-19 pandemic, electronic voting has gradually become an inseparable part of people's lives. However, it has also raised a series of severe privacy and trust challenges. The immutable and publicly transparent characteristics of blockchain are a perfect fit for the development of electronic voting systems, effectively eliminating voters' concerns about ballot tampering.At the same time, zero-knowledge proofs enable the prover to show they possess certain information to the verifier, without disclosing the actual details. It is important to note that with the rapid development of quantum technology, traditional cryptographic schemes face unprecedented security threats. To address this challenge, We present a quantum-resistant blockchain solution for electronic voting, incorporating zero-knowledge proofs. Compared to conventional elliptic curve-based zero-knowledge proof schemes, our proposed solution is based on RLWE, ensuring voter privacy, and uses BFV fully homomorphic encryption technology to implement a blockchain-based electronic voting protocol, ensuring the system’s high availability, security, and anonymous voting. Security analysis and performance testing, along with comparisons to existing similar solutions, show that our scheme has advantages in terms of security and robustness, making it highly practical.
The global shift towards sustainable transportation necessitates efficient and secure payment systems for electric vehicle (EV) charging on electrified roads. Current blockchain-based payment infrastructures face high transaction costs, inefficiencies, and security vulnerabilities, impeding EV adoption. To address these challenges, we propose a blockchain-based Vehicle Payment System (VPS) tailored for electrified roads. VPS integrates a hybrid consensus mechanism combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) for secure, decentralized, and efficient transaction validation. Scalability is enhanced through sharding, which distributes transaction load, while Zero-Knowledge Proofs (ZKPs) ensure transaction confidentiality, and multi-signature transactions provide additional security. State channels further optimize performance by enabling off-chain transactions, reducing congestion, and increasing throughput. Unlike prior research, which often neglects scalability, privacy, and real-time performance holistically, VPS achieves under 3000 ms latency for invoke transactions, under 450 ms for queries with 1000 users, and a throughput of approximately 1100 transactions per second (TPS) at a send rate of 1300. These advancements establish VPS as a scalable, efficient payment solution for EV charging, supporting the transition to green mobility and informing sustainable infrastructure policies.
Distributed Ledger Technologies (DLTs), including Blockchain, have revolutionized financial systems by offering decentralized, transparent, and secure mechanisms for data management and transactions. However, for these systems to maintain integrity and protect sensitive financial data, robust cryptographic techniques are essential. Cryptography ensures data confidentiality, authenticity, integrity, and non-repudiation, which are critical for the security of financial transactions in DLTs. This article examines the role of cryptographic protocols such as hashing, digital signatures, asymmetric encryption, and zero-knowledge proofs in safeguarding distributed ledgers. Furthermore, we explore their applications in securing financial transactions, preventing fraud, ensuring compliance, and enhancing the overall reliability of DLTs in financial systems. The discussion also delves into the challenges of cryptographic security in the face of emerging threats and the potential impact of quantum computing on existing cryptographic protocols.
M. K. Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
Alessandro Longo, Teodoro Criscione, Julio Linares, Sowelu Avanzo
Circles UBI is a blockchain-based Community Currency System (CCS) that has been active in Berlin (Germany) since October 2021. The Circles Coop, which launched the project in 2021, was shut down in December 2023. In this paper, we show the results of a survey carried out between October and November 2023. The respondents were twenty-five individuals involved in various ways in the Circles' network. The main emerging narrative points out how their participation was deeply motivated by their identification with the values and ideals of the Circles community. Among them, we selected five profiles that stood for their difference in type and degree of involvement. Finally, we report some stories of economic linkages that suggest a positive externality in adopting a local community currency. To our knowledge, this is the first qualitative study of a universal basic income designed as a community currency and adopting blockchain technology. This pilot project was a remarkable experiment for its adopted advanced technological and social innovations. In fact, as far as we know, the integration of basic income and local currency features has been experimented with only in two other cases (Maricá, Brazil and Barcelona, Spain) and none of them adopted a decentralized ledger system. In this work, we try to outline strengths and weaknesses that emerged after about two years of activity. For this reason, future researchers and activists interested in this field will find valuable information.
This paper introduces Web3DB, a decentralized relational database management system (RDBMS) designed to align with the principles of Web 3.0, addressing critical shortcomings of traditional centralized DBMS, such as data privacy, security vulnerabilities, and single points of failure. Several similar systems have been proposed, but they are not compatible with the legacy systems based on RDBMS. Motivated by the necessity for enhanced data sovereignty and the decentralization of data control, Web3DB leverages blockchain technology for fine-grained access control and utilizes decentralized data storage. This system leverages a novel, modular architecture that contributes to enhanced flexibility, scalability, and user-centric functionality. Central to the Web3DB innovation is its decentralized query execution, which uses cryptographic sortition and blockchain verification to ensure secure and fair query processing across network nodes. The motivation for integrating relational databases within decentralized DBMS primarily stems from the need to combine the robustness and ease of use of relational database structures with the benefits of decentralization. This paper outlines the architecture of Web3DB, its practical implementation, and the system's ability to support SQL-like operations on relational data, manage multi-tenancy, and facilitate open data sharing, setting new standards for decentralized databases in the Web 3.0 era.
This chapter explores three key questions in blockchain ethics. First, it situates blockchain ethics within the broader field of technology ethics, outlining its goals and guiding principles. Second, it examines the unique ethical challenges of blockchain applications, including permissionless systems, incentive mechanisms, and privacy concerns. Key obstacles, such as conceptual modeling and information asymmetries, are identified as critical issues. Finally, the chapter argues that blockchain ethics should be approached as an engineering discipline, emphasizing the analysis and design of trade-offs in complex systems.
Zhuoran Tan, Christos Anagnostopoulos, Jeremy Singer
Cyber supply chain, encompassing digital asserts, software, hardware, has become an essential component of modern Information and Communications Technology (ICT) provisioning. However, the growing inter-dependencies have introduced numerous attack vectors, making supply chains a prime target for exploitation. In particular, advanced persistent threats (APTs) frequently leverage supply chain vulnerabilities (SCVs) as entry points, benefiting from their inherent stealth. Current defense strategies primarly focus on prevention through blockchain for integrity assurance or detection using plain-text source code analysis in open-source software (OSS). However, these approaches overlook scenarios where source code is unavailable and fail to address detection and defense during runtime. To bridge this gap, we propose a novel approach that integrates multi-source data, constructs a comprehensive dynamic provenance graph, and detects APT behavior in real time using temporal graph learning. Given the lack of tailored datasets in both industry and academia, we also aim to simulate a custom dataset by replaying real-world supply chain exploits with multi-source monitoring.
Smart homes that integrate Internet of Things (IoT) devices face increasing cybersecurity risks, posing significant challenges to these environments. The study explores security threats in smart homes ecosystems, categorizing them into vulnerabilities at the network layer, device level, and those from cloud-based and AI-driven systems. Research findings indicate that post-quantum encryption, coupled with AI-driven anomaly detection, is highly effective in enhancing security; however, computational resource demands present significant challenges. Blockchain authentication together with zero-trust structures builds security resilience, although they need changes to existing infrastructure. The specific security strategies show their effectiveness through ANOVA, Chi-square tests, and Monte Carlo simulations yet lack sufficient scalability according to the results. The research demonstrates the requirement for improvement in cryptographic techniques, alongside AI-enhanced threat detection and adaptive security models which must achieve a balance between performance and efficiency and real-time applicability within smart home ecosystems.
The secure and efficient storage and sharing of medical images have become increasingly important due to rising security threats and performance limitations in existing healthcare systems. Centralized systems struggle to provide adequate privacy, rapid access, and reliable storage for sensitive medical images. This paper proposes a decentralized medical image-sharing framework to address these issues by integrating blockchain technology, the InterPlanetary File System (IPFS), and edge computing. Blockchain technology enforces secure patient-centric access control through smart contracts that enable patients to directly manage their data-sharing permissions. The IPFS provides decentralized and scalable storage for medical images and effectively resolves the storage limitations associated with blockchain. Edge computing enhances system responsiveness by significantly reducing latency through local data processing to ensure timely medical image access. Robust security is ensured by using elliptic curve cryptography (ECC) for secure key management and the Advanced Encryption Standard (AES) for encrypting medical images to protect against unauthorized access and data breaches. Additionally, the system includes real-time monitoring to promptly detect and respond to unauthorized access attempts to ensure continuous protection against potential security threats. System results demonstrate that the proposed framework achieves lower latency, higher throughput, and improved security compared to traditional centralized storage solutions, which makes our system suitable for practical deployment in modern healthcare settings.
Blockchain technology has emerged as a transformative solution for ensuring transparency, security, and immutability in student feedback management within OBE frameworks. Traditional feedback systems often suffer from inefficiencies, data manipulation risks, and lack of trust, necessitating the integration of decentralized and tamper-proof mechanisms. This book chapter explores the potential of blockchain and smart contracts in addressing these challenges by establishing a secure, transparent, and immutable student feedback system. The study examines the scalability limitations of blockchain networks and investigates advanced optimization techniques, including Layer 2 scaling solutions, sharding mechanisms, and hybrid storage models, to enhance performance and efficiency. The chapter highlights energy-efficient consensus protocols to improve sustainability in educational blockchain applications. Data availability challenges in off-chain storage and interoperability issues with existing LMS are also analyzed to ensure seamless adoption. By leveraging blockchain’s decentralized architecture, cryptographic security, and automated validation mechanisms, institutions can enhance the reliability and accountability of student feedback systems. The findings contribute to the ongoing discourse on blockchain applications in education, offering a scalable and efficient model for feedback management in OBE.
The form of money has been highly variable in history and continues to change drastically. New forms regularly appear, and their path to becoming money in a full sense is uncertain. Cryptocurrencies are a novel form of e-money based on distributed ledger technology and steadily growing in use. They are distinct from credit money but their own “moneyness” remains in doubt. This special issue of The Japanese Political Economy offers a political-economy-based analysis of their prospects. It considers deeper issues of validation, costs of use, and persistent speculation. It discusses functioning as unit of account, means of payment and reserve formation, and especially as world money. It also examines important differences between native coins with their own blockchains, Central Bank Digital Currencies, and stablecoins. Finally, the social character of cryptocurrencies is contrasted to community-based money. The jury is still out on whether cryptocurrencies could become money in a full sense, but it is shown that they have made great strides in that direction.
The integration of Artificial Intelligence (AI) and Blockchain technology has opened new possibilities in secure distributed systems, addressing several inherent challenges in cybersecurity, trust management, and data privacy. This article explores the synergies between AI and Blockchain, focusing on how they can complement each other in creating secure, transparent, and efficient distributed systems. AI's capabilities in predictive analytics, machine learning, and decision-making combined with Blockchain's decentralized, immutable ledger offer enhanced security and operational efficiency for applications ranging from financial transactions to healthcare and supply chain management. Through a comprehensive analysis of recent advancements and case studies, we highlight the practical implications of these technologies in fostering secure distributed systems and provide a roadmap for their future integration.
Privately created money based on Distributed Ledger Technology (DLT) emerged in the late 2000s at the same time as mobile money. The latter, exemplified by M-Pesa, has become a prevalent form of money in several countries, especially in Africa. DLT-based cryptocurrencies, in contrast, have achieved a rather limited monetary presence. This paper compares the creation and functioning of these two forms of digital money to establish reasons for the relatively weak social acceptability of cryptocurrencies. For the most prominent cryptocurrencies, such as Bitcoin and Ether, these reasons are shown to include deficiency as units of account, high costs of use, and fragmentation of blockchains. Ultimately, these are due to the decentralized and permissionless character of privately created DLT-based monies, which invites peculiar forms of capitalist profit making, including speculation. Despite its weaknesses, such money has the potential to become widely used, but that would require state intervention, which would alter its character.
This dissertation examines the financial dynamics of emerging digital assets by analyzing three pivotal domains: crypto futures, non-fungible tokens (NFTs), and the metaverse economy. Through empirical investigations and advanced modeling, it contributes to understanding how digital asset markets function, what drives their valuation, and how their future trajectories can be assessed. The first study explores price discovery between spot and futures markets for Bitcoin and Ether. By applying information share, component share, and information leadership share methodologies to high-frequency data, the analysis investigates whether futures markets dominate in incorporating new information or whether spot markets continue to lead. The findings reveal distinct dynamics between Bitcoin and Ether, with robustness checks confirming the persistence of futures-led price discovery in certain conditions, carrying implications for traders, exchanges, and regulators. The second study investigates determinants of NFT prices using an exploratory approach. Drawing on a comprehensive dataset, it tests how factors such as rarity, artist reputation, collection effects, and market sentiment influence valuation. By applying econometric and statistical modeling, the study uncovers that while scarcity and creator visibility play significant roles, speculative dynamics and network effects often dominate, leading to high volatility and difficulty in establishing intrinsic value. The third study addresses the question “Is the metaverse dead?” by focusing on Decentraland as a case study. Using Seasonal ARIMA and Log-Periodic Power Law Singularity (LPPLS) models, the research evaluates both the short-term forecasting accuracy and the existence of speculative bubbles in metaverse-related assets. The results indicate limited sustained user growth and significant bubble-like behavior in virtual land prices, highlighting the challenges of aligning metaverse narratives with economic realities. Together, these three essays provide a multifaceted view of digital asset markets. They demonstrate that while innovations such as crypto futures, NFTs, and metaverse platforms have transformed financial ecosystems, they remain highly speculative, vulnerable to bubbles, and subject to behavioral drivers beyond fundamental valuation. The dissertation offers theoretical contributions to financial economics and practical insights for investors, policymakers, and technologists navigating the uncertain terrain of digital asset markets.
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
Ever since the emergence of cryptocurrencies, scholars have grappled with the question of whether they are forms of money or not. The most interesting problem, however, is not if these instruments are already money, but whether they could become money. One crucial aspect in this regard is the potential (or lack thereof) of a privately-issued cryptocurrency to become the monetary unit of account. Drawing on Marx’s theory of money and making the hypothesis that cryptocurrencies are digital commodities, the article argues that cryptocurrencies create a unit of account (BTC) to describe a novel monetary instrument (a Bitcoin coin) aspiring to become a new form of world money. So far, they have not been widely used to denominate prices, incomes, or credits/debts except in certain, still limited but growing, areas of the on-chain digital world. Things could change if the use of cryptocurrencies spills over to the off-chain (digital and non-digital) world. Nevertheless, the adoption of cryptocurrencies as units of account would face several challenges in international and national circulation, crucially among them, the action of states to remain in control of the monetary unit.