Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it is important to consider the identification of changes across several trading networks or a `network of networks' over time. To this end, in this article, we propose a new change-point detection method in the network structure of tensor-variate data. This new method, labeled TenSeg, first employs a tensor decomposition, and second detects multiple change-points in the second-order (cross-covariance or network) structure of the decomposed data. It allows for change-point detection in the presence of frequent changes of possibly small magnitudes and is computationally fast. We apply our method to several simulated datasets and to a cryptocurrency dataset, which consists of network tensor-variate data from the Ethereum blockchain. We demonstrate that our approach substantially outperforms other state-of-the-art change-point techniques, and the detected change-points in the Ethereum data set coincide with changes across several trading networks or a `network of networks' over time. Finally, all the relevant \textsf{R} code implementing the method in the article are available on https://github.com/Anastasiou-Andreas/TenSeg.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) must reconcile an inherent tension. The Ethereum Virtual Machine (EVM) was designed for transparent step-by-step execution with dynamic control flow. Proving such execution in zero-knowledge, however, requires transforming it into algebraic circuit representations that encode computation as mathematical constraints. Existing surveys address zkEVMs at the level of implementations, cryptographic primitives, or Layer 2 deployment, leaving the constraint-system design that governs their cost largely unexamined. This survey provides the first constraint-level analysis of how five production zkEVM systems and three universal Zero-Knowledge Virtual Machines (zkVMs) resolve this tension through constraint engineering. We show that the degree of EVM compatibility, captured by the Type 1-4 spectrum, is the defining architectural decision that shapes all subsequent technical choices. We classify the design space along four architectural dimensions, namely arithmetization frameworks, dispatch strategies, semantic rewrites, and recursion approaches. Examining the mechanisms within each dimension, we identify the technical factors and trade-offs that drive each choice. The analysis reveals that all five surveyed production zkEVMs adopt PLONKish arithmetization. The zkVMs instead rely on the Algebraic Intermediate Representation (AIR), which suits uniform state machines. A single trade-off between EVM compatibility and constraint cost underlies these choices. The most Ethereum-equivalent systems accept higher constraint counts to preserve full bytecode fidelity, while systems that relax that fidelity attain substantially lower constraint counts. We close with the critical open problems and future research directions that this constraint-level view brings into focus.
Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging blockchain and artificial intelligence technologies, Web 3.0 offers unprecedented opportunities for users to create, own, and monetize their content, thereby enabling User-Generated Content (UGC) to an entirely new level. However, some self-interested users may exploit the limitations of content curation mechanisms and generate low-quality content with less effort, obtaining platform rewards under information asymmetry. Such behavior can undermine Web 3.0 performance. To this end, we propose \textit{LMM-Incentive}, a novel Large Multimodal Model (LMM)-based incentive mechanism for UGC in Web 3.0. Specifically, we propose an LMM-based contract-theoretic model to motivate users to generate high-quality UGC, thereby mitigating the adverse selection problem from information asymmetry. To alleviate potential moral hazards after contract selection, we leverage LMM agents to evaluate UGC quality, which is the primary component of the contract, utilizing prompt engineering techniques to improve the evaluation performance of LMM agents. Recognizing that traditional contract design methods cannot effectively adapt to the dynamic environment of Web 3.0, we develop an improved Mixture of Experts (MoE)-based Proximal Policy Optimization (PPO) algorithm for optimal contract design. Simulation results demonstrate the superiority of the proposed MoE-based PPO algorithm over representative benchmarks in the context of contract design. Finally, we deploy the designed contract within an Ethereum smart contract framework, further validating the effectiveness of the proposed scheme.
Centralized exchanges (CEXs) currently dominate the cryptocurrency trading landscape due to their speed, liquidity, and ease of use. However, they also introduce several critical risks, including custodianship of user assets, vulnerability to censorship, and reliance on centralized infrastructure that represents a single point of failure. In contrast, the advent of Automated Market Makers (AMMs), such as Uniswap, brought a paradigm shift in decentralized finance (DeFi) by enabling peer-to-peer trading through liquidity pools without intermediaries. While revolutionary, AMMs face inherent limitations such as slippage, impermanent loss for liquidity providers, and suboptimal price discovery compared to traditional orderbook systems. This research proposes a decentralized on-chain orderbook model designed to bridge the gap between centralized exchanges and AMM-based decentralized exchanges. The system replicates the precision, transparency, and efficiency of traditional orderbookdriven markets while adhering to DeFi principles of trustlessness and non-custodial asset management. Developed using Solidity smart contracts and deployed on Ethereum-compatible test networks such as Monad the platform enables users to place, cancel, and execute both limit and market orders directly on-chain. To address blockchain performance bottlenecks, the architecture incorporates an off-chain order matcher that listens to smart contract events, identifies compatible buy and sell orders, and batches potential matches for improved gas efficiency. Importantly, final trade execution and settlement remain fully decentralized, being handled exclusively by smart contracts. This hybrid design achieves low-latency order matching without compromising decentralization or asset security
The Metaverse has evolved into a transformative ecosystem, merging virtual and physical realities with consumer electronics and IoT to enable immersive experiences. However, vulnerabilities like avatar impersonation, identity theft, and Sybil attacks, compounded by centralized intermediaries, underscore the urgent need for decentralized solutions to enhance interoperability, security, and user-centricity. This paper proposes a smart contract-based authentication scheme for the Metaverse, leveraging Web 3.0 technologies to achieve decentralization and address existing limitations. The scheme ensures user-server and avatar-avatar authentication while maintaining anonymity, unlinkability, and incorporating an avatar revocation mechanism. Security validation is conducted using the informal analysis, and smart contract implementation. The systemâs operational efficiency is benchmarked against existing solutions using metrics such as computational, bandwidth, and communication costs. Results demonstrate the proposed schemeâs superior performance in reducing computational and communication overhead, making it highly suitable for Metaverse applications. Additionally, the smart contract is implemented on the Ethereum test network, confirming its feasibility and showcasing an acceptable cost for blockchain consumption.
Svitlana Popereshnyak, Dmytro Chornobryvets, Oleh Bakaiev
The accelerated growth of freelance platforms has brought to light several systemic challenges, such as elevated transaction costs, increased susceptibility to fraud, limited transparency, and inefficiencies in the selection of service providers. This study presents the design and implementation of an AI-powered platform aimed at improving the management and monitoring of freelance services. The platform architecture incorporates a multi-criteria risk assessment framework, which evaluates users based on their ratings, transaction history, account longevity, and digital wallet balance. To address issues of contractor reliability and operational anomalies, the system integrates advanced algorithms for automated selection and anomaly detection. A smart contract mechanism, implemented in Solidity and deployed on the Ethereum blockchain via Web3.js, ensures secure and verifiable transactions. For data storage and retrieval, the platform leverages PostgreSQL and MongoDB, while ECDSA cryptographic techniques are employed to reinforce transaction integrity and user authentication. Empirical evaluation indicates that the platform substantially mitigates fraud risks and enhances the efficiency and transparency of interactions between clients and freelancers. The proposed solution demonstrates the potential to support secure and scalable freelance operations and may be extended for deployment within decentralized finance ecosystems and digital commerce environments.
We introduce a modified Schnorr signature scheme to allow for time-bound signatures for transaction fee auction bidding and smart contract purposes in a blockchain context, ensuring an honest producer can only validate a signature before a given block height. The immutable blockchain is used as a source of universal time for the signature scheme. We show the use of such a signature scheme leads to lower MEV revenue for builders. We then apply our time-bound signatures to Ethereum's EIP-1559 and show how it can be used to mitigate the effect of MEV on predicted equilibrium strategies.
Cryptocurrencies have revolutionized the financial landscape, introducing decentralized digital assets like Bitcoin and Ethereum. Their growth has spurred interest in statistical methods for monitoring and analyzing transactions, especially in the context of traditional financial systems like SWIFT (Society for Worldwide Interbank Financial Telecommunication). Statistical methods play a crucial role in identifying patterns, anomalies, and potential risks associated with cryptocurrency transactions. These methods involve data analysis, clustering, and machine learning algorithms to detect fraudulent activities, money laundering, and market trends. The integration of blockchain technology ensures transparency and immutability, enhancing statistical analysis accuracy.On the other hand, SWIFT transactions, widely used for cross-border payments, rely on statistical techniques to track and validate international fund transfers. These methods aid in fraud detection, regulatory compliance, and transaction efficiency. Combining the statistical prowess of cryptocurrencies and SWIFT transactions offers a comprehensive approach to secure and efficient global finance.In conclusion, cryptocurrencies have emerged as a disruptive force in the world of finance, offering decentralized, secure, and borderless transactions. Their popularity has grown exponentially, attracting both enthusiasts and skeptics. They have disrupted traditional finance, offering decentralized digital assets like Bitcoin and Ethereum. Statistical methods are crucial for monitoring and securing transactions on the SWIFT network, the backbone of global financial messaging. One of the recommendations was that advanced data analytics to detect anomalies, trend analysis for fraud prevention, and machine learning algorithms for predictive modeling.
Proof-of-stake blockchains require consensus protocols that support Dynamic Availability and Reconfiguration (so-called DAR setting), where the former means that the consensus protocol should remain live even if a large number of nodes temporarily crash, and the latter means it should be possible to change the set of operating nodes over time. State-of-the-art protocols for the DAR setting, such as Ethereum, Cardano's Ouroboros, or Snow White, require unrealistic additional assumptions, such as social consensus, or that key evolution is performed even while nodes are not participating. In this paper, we identify the necessary and sufficient adversarial condition under which consensus can be achieved in the DAR setting without additional assumptions. We then introduce a new and realistic additional assumption: honest nodes dispose of their cryptographic keys the moment they express intent to exit from the set of operating nodes. To add reconfiguration to any dynamically available consensus protocol, we provide a bootstrapping gadget that is particularly simple and efficient in the common optimistic case of few reconfigurations and no double-spending attempts.
The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting in significant financial losses for investors. Currently, there is a lack of effective methods for identifying and analyzing such new types of fraudulent activities. This paper categorizes these scams into four structural types and explores the intrinsic characteristics of Ponzi scheme contract source code from a program analysis perspective. The Mythril tool is employed to conduct static and dynamic analyses of representative cases, thereby revealing their vulnerabilities and operational mechanisms. Furthermore, this paper employs shell scripts and command patterns to conduct batch detection of open-source smart contract code, thereby unveiling the common characteristics of Ponzi scheme smart contracts.
Interoperability between blockchain platforms remains a key challenge, particularly in sensitive domains such as healthcare, where the secure and consistent exchange of clinical information between institutions is essential. While technical interoperability solutions exist, semantic interoperability at the level of smart contracts continues to be a significant limitation. This paper presents MUISCA, a mechanism based on Model-Driven Engineering that enables the automatic generation of interoperable smart contracts across different blockchain platforms. By defining metamodels, abstract models, and transformation rules, MUISCA produces platform-specific code for technologies such as Ethereum and Hyperledger Fabric. The mechanism was validated through a healthcare case study focused on patient transfers between medical institutions, demonstrating its ability to support the secure exchange of clinical data. Additionally, its acceptance was evaluated through expert surveys assessing perceived usefulness and ease of use. Results show that MUISCA improves smart contract portability, reduces implementation errors, and enhances system security. The proposed solution contributes to advancing semantic interoperability in blockchain-based health information systems and provides a foundation for broader application in other critical domains that require high levels of integration and data protection.
Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future.
Samuel A. Oyenuga, Brendan Ubochi, Okechi Onuoha, Nnamdi Nwulu
The rapid growth in IoT applications has brought enormous challenges especially with achieving scalability and security in communicating devices.Traditional centralized security models are inadequate for managing the vast volume of data and diverse communication protocols in IoT environments, making them vulnerable to attacks such as Distributed Denial of Service (DDoS) and unauthorized access.Blockchain technology offers a decentralized alternative with its inherent properties of immutability, transparency, and decentralized consensus, providing a robust security solution for IoT communication.This paper presents a novel blockchain-based framework designed to secure IoT communication by addressing key challenges such as data integrity, privacy, and scalability.The proposed system integrates Ethereum's blockchain, Zero Knowledge (ZK)-Rollups for Layer 2 scaling, and edge computing to optimise both performance and energy efficiency in large-scale IoT networks.The framework achieves a transaction throughput of 2,500 transactions per second with a median latency of 850 milliseconds.ZK-Rollups ensure that 99.8% of transactional data remains off-chain, improving privacy while reducing computational overhead.The system maintains 99.7% uptime during DDoS attacks and reduces energy consumption by 95% compared to traditional Proof of Work (PoW) blockchain systems.These findings indicate that the proposed blockchain-based framework is scalable, energyefficient, and secure, making it a promising solution for large-scale IoT deployments in sectors such as smart cities, industrial automation, and healthcare.
This thesis explores two complementary areas in finance: securities tokenization and interbank payments. First, we propose a tokenization framework extending decentralized finance (DeFi) benefitsâaccessibility, transparency, efficiencyâto real-world securities. While DeFi mechanisms like liquidity pools complicate securities entitlements (e.g., dividends, voting), our solution overcomes this by combining fungible tokens with off-chain accounting and separate smart contracts for entitlements. Implemented on Ethereum, it saves 27% in costs compared to alternatives and supports various securities including stocks and bonds. We also confirm compatibility with liquidity logic in 90% of Ethereum pools. Second, we model Real Time Gross Settlement (RTGS)-based Large Value Payment Systems (LVPS) using queuing theory. RTGS is the most common mechanism for LVPS due to its instant settlement and low risk, but it has high liquidity requirements. Our model yields closed-form solutions for network performance and offers insights into liquidity requirements to aid the design of liquidity saving mechanisms.
Moderne Blockchains verarbeiten mittlerweile Zehntausende Transaktionen pro Sekunde. Mit steigendem Durchsatz wachsen jedoch auch die Anforderungen fĂŒr die Verifikation von Blockchains. Zentralisierte Node-as-a-Service (NaaS)-Anbieter (z.B. Infura oder Alchemy) bieten zwar praktische APIs, schaffen jedoch zusĂ€tzliche VertrauensabhĂ€ngigkeiten und bergen Risiken in Bezug auf Datenschutz und Zensurfreiheit. Ein selbst betriebener Full Node ermöglicht Datenzugang ohne zusĂ€tzliche Vertrauensannahmen, ist fĂŒr die meisten Nutzerinnen und Nutzer jedoch aufgrund des hohen Ressourcenbedarfs kaum praktikabel. Im Gegensatz dazu arbeiten Light Clients deutlich ressourcenschonender, können dafĂŒr den vollstĂ€ndigen Anwendungszustand nicht rekonstruieren. Ein neuer Ansatz, der als Sparse Client (bzw. Partially Stateless Client) bekannt ist, ermöglicht dagegen die verifizierbare Ăberwachung eines Teilzustands der Blockchain, indem ausschlieĂlich jene Transaktionen heruntergeladen, ausgefĂŒhrt und gespeichert werden, die diesen Teilzustand lesen oder verĂ€ndern. Bisher fehlt eine fundierte wissenschaftliche Aufarbeitung: Die einzige verfĂŒgbare Arbeit zu diesem Thema weist deutliche Limitierungen auf und wurde weder implementiert noch umfassend evaluiert. In dieser Arbeit prĂ€sentieren wir zwei Sparse-Client-Protokolle fĂŒr EVM-kompatible Blockchains: Sparseth fĂŒr zustandsbasierte Synchronisation und Eventeth fĂŒr ereignis-basierte Synchronisation. Beide Protokolle ermöglichen es Nutzerinnen und Nutzern, ĂŒberprĂŒfbare Teilmengen der globalen Transaktions- oder Ereignissequenz und des damit verbundenen Zustands zu verwalten, ohne dass zusĂ€tzlicher Validator-Aufwand erforderlich ist. Sparseth nutzt einen InteraktionszĂ€hler, um sicherzustellen, dass keine relevanten Transaktionen ausgelassen werden, wĂ€hrend Eventeth eine kryptographische Hash-Kette einsetzt, um die IntegritĂ€t und VollstĂ€ndigkeit der Ereignisse zu gewĂ€hrleisten. Im Gegensatz zu bestehenden AnsĂ€tzen arbeiten beide Protokolle vollstĂ€ndig auf der AusfĂŒhrungsschicht und sind mit EVM-basierten Blockchains kompatibel. Unsere formale Analyse zeigt, dass beide Protokolle im angenommenen Widersacher-Modell Sicherheit, Liveness und spĂ€rliche GĂŒltigkeit garantieren. Unsere Implementierung in Go demonstriert die praktische Umsetzbarkeit: Event Nodes senken den Bandbreitenbedarf um ĂŒber 95%, Sparse Nodes reduzieren die auszufĂŒhrenden Transaktionen um 92% gegenĂŒber Full Nodes. Die Gas-Kosten steigen um 4-16% fĂŒr typische dApp-Transaktionen, ein Mehraufwand, der sich durch L2-Lösungen und ökonomische Anreize weiter mindern lĂ€sst.
PURCAREA LIVIU, RADULESCU CARMEN VALENTINA, MÄnescu Andreea Maria
Blockchain technology has rapidly transformed the way decentralized systems operate, offering new possibilities for transparency, security, and autonomy. However, these benefits come with a notable drawback: the significant environmental cost associated with blockchain consensus mechanisms â particularly Proof of Work (PoW) Möser et all (2021). This paper examines the environmental impact of blockchain and investigates more sustainable alternatives, such as Proof of Stake (PoS) and other energy-efficient models. Using Ethereumâs transition from PoW to PoS as a central case study, along with examples such as Algorand and Chia, we explore how different architectural decisions affect energy consumption. Our analysis, based on recent academic research and technical data, suggests that sustainable blockchain models are technically viableâbut their success depends on broader systemic changes, including clear regulations, governance reforms, and industry-wide engagement. In conclusion, blockchain can evolve into a sustainable technology, but only through a responsible and coordinated effort
Luigi Coppolino, Giovanni Maria Cristiano, Salvatore DâAntonio, Jonah Giglio · 6 authors
Ensuring the reliability and accuracy of information is a critical challenge in sectors like finance, media, and health. The reliance on centralized verification systems introduces risks of bias, manipulation, and limited transparency. To address these issues, we propose VeriNet , a decentralized framework for third-party content verification leveraging blockchain technology and the Ethereum Attestation Service. VeriNet integrates on-chain and off-chain attestations to ensure privacy, transparency, and accountability, supported by a Decentralized Data Warehouse and cryptographic Proof-of-SQL mechanisms. The framework includes Contributors , who submit content, and Verifiers , who assess its authenticity. We carried out two Proof-of-Concept implementations, namely in deepfake detection and fintech credit scoring, to demonstrate the efficiency of VeriNet and its adaptability to diverse domains. Moreover, we conducted an experimental evaluation focusing on various parameters, such as costs and execution time, to demonstrate framework feasibility, scalability, and potential to establish a trusted ecosystem for collaborative verification.
Healthcare data management in cloud environments faces critical challenges in balancing security, privacy, operational efficiency, and regulatory compliance, as traditional access control mechanisms are inadequate for addressing the complex, multi-tenant, and dynamic nature of modern cloud infrastructures handling sensitive Electronic Health Records (EHRs). This paper introduces a novel hybrid framework that integrates Attribute-Based Access Control (ABAC) with Ethereum blockchain technology to establish a comprehensive multi-layered security architecture for cloud-based healthcare systems, combining advanced cryptographic techniques including double encryption using AES and RSA algorithms, Shamir's Secret Sharing for key management, smart contracts for automated policy enforcement, and multi-party computation protocols to ensure privacy preservation without compromising system performance. Extensive experimental evaluation conducted using 4,412 anonymized clinical records with 12 distinct attributes demonstrates exceptional efficiency with average record insertion time of 1.90 ms, retrieval time of 0.14 ms, blockchain mining time of 0.01 seconds, and hybrid architecture throughput of 562.94 records/second with 1.78 ms latency, significantly outperforming traditional centralized systems (337.17 rec/s, 2.97 ms) and pure blockchain implementations (3.37 rec/s, 296.98 ms). Security assessments reveal 100% operational reliability, 97.3% policy violation detection accuracy, 89% reduction in unauthorized access attempts, and comprehensive regulatory compliance with HIPAA (97%), GDPR (92%), ISO 27001 (94%), and SOC 2 (89%) standards, while maintaining optimized system resource utilization with CPU usage at 35.6%, memory at 48.0%, and network I/O at 34.1%. The framework addresses key limitations of existing approaches by providing seamless integration across multiple cloud layers, eliminating single points of failure, ensuring data immutability through distributed consensus mechanisms, maintaining comprehensive audit trails, and offering practical implications for healthcare organizations seeking secure, scalable, and compliant data sharing mechanisms in cloud-native environments, thereby contributing to the advancement of secure healthcare informatics by bridging the gap between theoretical access control models and practical implementation requirements in distributed cloud infrastructures.
Ruben M Nayve Jr, Ferdinand Timbang, Mark Anthony Pelegrin
This study investigates the role of network effects in shaping economic value creation within cryptocurrency ecosystems. While cryptocurrencies have evolved from speculative assets into complex digital platforms that facilitate transactions, decentralized finance, and governance, their economic sustainability is fundamentally driven by the dynamics of user adoption and interconnectivity. Drawing upon theories of network externalities and digital platform economics, this research employs a mixed-methods approach that integrates quantitative econometric modeling with qualitative analysis of policy and industry practices. Quantitative data, including market capitalization, transaction volume, wallet addresses, and hash rate, are analyzed to measure the correlation between network growth and value creation. Complementary qualitative insights are derived from literature reviews and expert interviews to contextualize the findings within broader regulatory and technological frameworks. The results reveal that network size and user activity exert significant positive effects on value creation, with evidence of nonlinear threshold dynamics: once a critical mass of adoption is reached, economic value accelerates disproportionately. Comparative analysis across major ecosystems such as Bitcoin and Ethereum highlights differences in how network effects interact with technological innovation and governance structures. The findings contribute to advancing theoretical understanding of digital network economies and provide practical insights for stakeholders, including developers, investors, and policymakers. By identifying both opportunities and risks, particularly regarding volatility and regulatory challenges, this study offers a comprehensive framework for evaluating the long-term sustainability of cryptocurrency ecosystems.
The aim of the study was to conduct a comprehensive comparison of selected popular cryptocurrencies, such as Bitcoin, Ethereum, XRP, Cardano, and Litecoin. The analysis was carried out in terms of technological categories, scalability, as well as security and privacy. In addition, the development prospects of selected cryptocurrencies were assessed.The research showed that despite its slow transaction speed and scalability issues, Bitcoin is the most widely accepted cryptocurrency as a form of payment, while XRP, which has the fastest transaction speed, is not as widely chosen and supported as a payment method. In terms of everyday applications, Ethereum is the best choice due to its ability to automate many business processes. Most cryptocurrencies are decentralized, which is the foundation of their existence and functioning. Bitcoin, Litecoin, and Cardano place considerable emphasis on this, while there is much debate about the actual decentralization of XRP due to their affiliation with Ripple Labs. An analysis of price stability and volatility has shown that a large proportion of cryptocurrencies are dependent on Bitcoin's market position, which gives an idea of how important it is as a resource. In terms of potential future applications, each of the cryptocurrencies studied can be used as a means of payment, but Bitcoin and Ethereum have the potential to find general use. In addition, they are seen by investors as the safest investment option due to the constant improvements and updates.Analysis in terms of selected factors, including technology, privacy, stability, and price volatility, allowed us to determine the relationship between these factors and the development prospects of the given cryptocurrencies. Bitcoin and Ethereum are the most popular currencies due to their price, and investors often treat them as long-term investments with high growth potential. XRP, Cardano, and Litecoin are less commonly used due to their high dependence on Bitcoin and low growth potential compared to other cryptocurrencies.
This study analyzes the time-varying interactions among assets in the digital financial asset market. Within the scope of the study, 1,820 daily observations from the 2020â2025 period for Ethereum, Ripple, Binance Coin, Cardano, Stellar, IOTA, Stacks, and Chainlink are examined using the Generalized RÂČ method proposed by Balli et al. (2023). This approach reveals both contemporaneous and lagged interconnectedness between assets, thereby enabling an understanding of how dynamic relationships evolve over time. The results indicate that market interconnectedness is not stable over time and that the transmission of shocks tends to intensify particularly during periods of uncertainty. The findings show that Ethereum maintained a central role throughout the analysis period, while Cardano, STX, LINK, and IOTA were more exposed to shocks. These results underscore the necessity of policy frameworks that address not only individual asset risks but also contagion risks to promote market stability. From an investorâs perspective, it is recommended that portfolio compositions consider both contemporaneous and lagged effects.
Atul Kumar Singh, Nishanth Rao Dugyala, Farzad Pour Rahimian, Faris Elghaish · 5 authors
Existing ESG reporting tools in construction organizations often lack transparency and accountability, presenting significant challenges in effectively managing and reporting ESG data. This research addresses the gap in current reporting practices by proposing and validating a hybrid blockchain solution aimed at enhancing ESG reporting in the Architecture, Engineering, and Construction (AEC) industry. The primary objective is to develop a blockchain-based solution that automates ESG reporting, addressing issues such as data fragmentation, lack of verification, and inefficiencies. Adopting a design science approach, the study develops a conceptual framework that combines Ethereum and Hyperledger Fabric to create a hybrid blockchain model for the prototype. The comprehensive literature review highlights key challenges in ESG practices and emphasizes the potential of blockchain technology to overcome these barriers. The findings show that the hybrid blockchain model successfully automates the ESG reporting process, ensuring transparency, immutability, and accountability. The prototype, validated through a case study involving two construction organizations, demonstrates the feasibility of combining Ethereum and Hyperledger Fabric to manage ESG data, reducing errors, preventing manipulation, and enabling real-time reporting. This research enriches the theoretical understanding of blockchain applications in ESG practices. It provides practical implications by offering a tangible, blockchain-based solution that ensures transparent, reliable, and accountable ESG reporting in the construction industry, ultimately contributing to more sustainable practices.
Maruf Farhan, Usman Butt, Rejwan Bin Sulaiman, Mansour Naser Alraja
The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms.