Widespread adoption of cryptocurrency on a global scale has created new and unique challenges for lawmakers. The rise of decentralized digital assets are reshaping finance, trade and investment, and the absence of consistent regulation exposes consumers to fraud and money laundering, market manipulation, and consumer protection adherence risks. In this paper, we have gauged the available legal tools in the cryptocurrency era and have reviewed cross-border regulatory regimes, jurisdictional challenges, and technological responses and have identified the road to a credible regulatory framework. Relying upon qualitative analysis and a comparative legal perspective, this article underscores the necessity that for the preservation of stability and security of digital financial systems the right mix of innovation and regulation ought to be struck.
Atomic swaps represent a groundbreaking advancement in decentralized asset exchange, enabling peer-to-peer trading of cryptocurrencies across different blockchains without the need for intermediaries. By utilizing smart contract technology, these swaps allow users to securely trade assets in a trustless environment, removing the need for centralized exchanges. This paper explores the technical underpinnings of atomic swaps, the benefits they offer over traditional exchange models, and their potential to shape the future of cross-chain transactions. We examine the core mechanisms of hash time-locked contracts (HTLCs), the role of liquidity pools, and the scalability challenges inherent in atomic swap technology. Furthermore, the paper discusses the broader implications of atomic swaps for decentralized finance (DeFi), financial sovereignty, and the global cryptocurrency ecosystem. As the market for decentralized exchanges continues to expand, atomic swaps may emerge as a vital tool for enhancing the efficiency, security, and inclusivity of digital asset trading.
Jesus Gama-Rodnguez, Ekam Puri Nieto, Juan Francisco Martínez Gil, Agustín Marín Frutos
Exploration in the crucial role of cyber threat intelligence (CTI) sharing and lifecycle security in IoT ecosystems. It examines how the ERATOSTHENES project leverages distributed ledger technology (DLT) and an inter-ledger approach to facilitate secure and privacy-preserving CTI exchange across different domains. The chapter also discusses the use of Manufacturer Usage Description (MUD) files, including the proposed Threat MUD extension, to manage security configurations and mitigation actions throughout the device lifecycle. Additionally, it highlights the integration of these components to achieve a system that dynamically responds to cybersecurity incidents, ensuring the ongoing protection of devices and domains.
Abstract This article discusses four significant changes in lesson drawing, policy learning, or policy borrowing. Thematically , the issue of educational expansion has preoccupied policy-makers worldwide for the past hundred years. They have been eager to learn from experiences elsewhere, initially how others decentralized the finance and management of schooling to enable universal access and later how they addressed the fallout of decentralization reforms, notably inequity and quality erosion. Regarding the modalities of policy learning, the ‘travelling observer’ has been replaced by myriad digital platforms that propagate best practices and ‘actionable’ policy recommendations. As a consequence of the decentralization of governance and finance and, in some countries, structural adjustment policies, the drivers of policy borrowing have shifted from ministries of education to a wide range of stakeholders, including ministries of finance, offices of presidents and prime ministers, and private foundations and businesses. Finally, changes in the objects of emulation or reference societies reflect a spatial reconfiguration of a special sort. Along with countries in the same geopolitical or cultural space, the education systems of league leaders in international large-scale assessments, such as Finland, Korea, and Singapore, have become objects of policy attraction for both OECD and non-OECD countries.
The financial sector in the 21st century is experiencing a revolution. The major disruptor is decentralised finance (DeFi) which leverages emerging blockchain technology to eliminate the need for centralised financial institutions and empowers individuals with peer-to-peer digital exchanges. DeFi is underpinned by cryptoassets such as bitcoin, ether, and non-fungible tokens (NFTs). As DeFi offerings have become increasingly sophisticated, important legal issues have arisen. One such issue is whether the law is appropriately positioned to recognise and give effect to the use of cryptoassets as collateral in lending arrangements. The lack of legal certainty at present poses a substantial risk to market participants who are, for the most part, transacting blindly. This article, therefore, addresses the applicability and comparative suitability of New Zealand's Personal Property Securities Act 1999 (PPSA) to cryptoasset collateral, using the recent Singaporean case of Chefpierre as a test case. It argues that the PPSA is generally better positioned than English (Singaporean) secured credit law to respond to the emerging use of cryptoassets as collateral. Nevertheless, the challenges posed by cryptoasset collateral necessitate legislative change; in particular, change to the PPSA's perfection requirements and priority rules. After reviewing and analysing recent legal developments in the United Kingdom and the United States, this article proposes that a number of bespoke rules and concepts designed to respond to cryptoassets be introduced into the PPSA.
As blockchain technology matures, the once-isolated networks that powered individual cryptocurrencies are now converging through a new wave of interoperability solutions. Cross-chain protocols—systems that enable different blockchains to communicate and share data or assets—are rapidly reshaping the decentralized landscape. These innovations address one of the most critical limitations of early blockchain architecture: siloed ecosystems that cannot seamlessly interact. This paper explores the evolution, technical mechanisms, and real-world implementations of cross-chain interoperability, including token bridges, relays, and multi-chain platforms such as Polkadot, Cosmos, and Layer Zero. We examine how these protocols are unlocking new levels of liquidity, composability, and user experience across decentralized finance (DeFi), gaming, and supply chain applications. While the promise of a fully interoperable blockchain future is compelling, we also discuss the security risks, fragmentation challenges, and the ongoing tension between decentralization and performance. Ultimately, this research highlights the pivotal role of interoperability in the next phase of blockchain adoption and outlines the key players and technologies driving this transition.
Alexandre Pires Barbosa, Rodrigo Pereira dos Santos
O aquecimento global é o maior desafio da humanidade e a ciência da computação tem papel central na escalabilidade de soluções de impacto social. Organizações autônomas descentralizadas (do inglês, decentralized autonomous organization or DAO) se refere a um tema emergente que vem ganhando destaque devido ao crescimento de iniciativas colaborativas. Este trabalho propõe a investigação de governança descentralizada em ecossistemas de inovação social, com objetivo de acelerar a mitigação das emissões de gases de efeito estufa por meio da participação social. Potenciais modelos como artefatos resultantes podem estabelecer diretrizes e expressar relacionamentos entre os envolvidos para orquestrar diferentes interações por meio das DAO.
We develop and apply epistemic tests to various decentralized governance methods as well as to study the impact of participation. These tests probe the ability to reach a correct outcome when there is one. We find that partial abstention is a strong governance method from an epistemic standpoint compared to alternatives such as various forms of ``transfer delegation" in which voters explicitly transfer some or all of their voting rights to others. We make a stronger case for multi-step transfer delegation than is present in previous work but also demonstrate that transfer delegation has inherent epistemic weaknesses. We show that enhanced direct participation, voters exercising their own voting rights, can have a variety of epistemic impacts, some very negative. We identify governance conditions under which additional direct participation is guaranteed to do no epistemic harm and is likely to increase the probability of making correct decisions. In light of the epistemic challenges of voting-based decentralized governance, we consider the possible supplementary use of prediction markets, auctions, and AI agents to improve outcomes. All these results are significant because epistemic performance matters if entities such as DAOs (decentralized autonomous organizations) wish to compete with organizations that are more centralized.
G. Sharmila, K. Neha, M. Kaviya, M. Juhe Sherin · 5 authors
Blockchain technology is a cutting-edge advancement in information technology. Bitcoin, as one of its initial uses, has attracted considerable attention as a cryptocurrency. Alongside Ethereum, which emphasizes blockchain-driven smart contracts, these technologies lie at the heart of modern cryptocurrency innovation. Off-chain transactions offer a scalable solution for blockchain networks, reducing congestion, lowering transaction fees, and improving processing efficiency without compromising decentralization. However, existing off-chain solutions often face security and flexibility challenges, particularly in environments with high latency and unstable connectivity. The proposed system leverages the Hardhat blockchain framework with Ethereum to enable secure peer-to-peer transactions from user wallets, ensuring seamless fund transfers even in offline conditions. Additionally, it integrates blockchain-based email functionality, allowing encrypted messages to be sent securely over a decentralized network, thereby enhancing data privacy and security. To further strengthen data integrity, the system incorporates the Inter Planetary File System (IPFS) for decentralized file storage, reducing reliance on centralized servers and minimizing data loss risks. By combining off-chain transactions, blockchain-based email, and IPFS storage, the system enhances efficiency, security, and reliability, offering a robust decentralized solution for financial transactions and secure communication. The data is distributed across all cryptocurrency users within the network. This ensures that when a user initiates a transaction, data mining processes are conducted.
AI and DLT can play a transformative role when it comes to supply chain visibility and accountability. Artificial intelligence can be utilized to improve predictive analytics, demand forecasting, and automation of operations, resulting in real-time, data-driven decision-making. However, while DLT provides security, immutability, traceability, and decentralized control that enhance transparency, reduce the possible of fraud and build trust among diverse parties across the supply chain. This paper discusses the symbiosis of both technologies and will demonstrate how their emerging capabilities deliver end-to-end visibility, streamlining operations and improving goods traceability throughout supply chain logistics. Case studies from diverse industries including manufacturing, logistics, and retail highlight the tangible implementation and benefits of these technologies in real-world settings. The paper also emphasizes the difficulties and constraints encountered during the deployment of AI and DLT technologies, with specific mention to interoperability, scalability, and data privacy issues. Also it outlines a path for organizations to embrace such technologies and better integrate them into their supply chains, increasing efficiency, accountability, and competitive advantage.
Shamnad Mohamed Shaffi, Sunish Vengathattil, Jezeena Nikarthil Sidhick, Resmi Vijayan
Cloud security concerns have been greatly realized in recent years due to the increase of complicated threats in the computing world. Many traditional solutions do not work well in real-time to detect or prevent more complex threats. Artificial intelligence is today regarded as a revolution in determining a protection plan for cloud data architecture through machine learning, statistical visualization of computing infrastructure, and detection of security breaches followed by counteraction. These AI-enabled systems make work easier as more network activities are scrutinized, and any anomalous behavior that might be a precursor to a more serious breach is prevented. This paper examines ways AI can enhance cloud security by applying predictive analytics, behavior-based security threat detection, and AI-stirring encryption. It also outlines the problems of the previous security models and how AI overcomes them. For a similar reason, issues like data privacy, biases in the AI model, and regulatory compliance are also covered. So, AI improves the protection of cloud computing contexts; however, more efforts are needed in the subsequent phases to extend the technology's reliability, modularity, and ethical aspects. This means that AI can be blended with other new computing technologies, including blockchain, to improve security frameworks further. The paper discusses the current trends in securing cloud data architecture using AI and presents further research and application directions.
Inconsistent outputs and hallucinations from large language models (LLMs) are major obstacles to reliable AI systems. When different proprietary reasoning models (RMs), such as those by OpenAI, Google, Anthropic, DeepSeek, and xAI, are given the same complex request, they often produce divergent results due to variations in training and inference. This paper proposes a novel consensus mechanism, inspired by distributed ledger technology, to validate and converge these outputs, treating each RM as a black-box peer. Building on the Hashgraph consensus algorithm, our approach employs gossip-about-gossip communication and virtual voting to achieve agreement among an ensemble of RMs. We present an architectural design for a prototype system in which RMs iteratively exchange and update their answers, using information from each round to improve accuracy and confidence in subsequent rounds. This approach goes beyond simple majority voting by incorporating the knowledge and cross-verification content of every model. We justify the feasibility of this Hashgraph-inspired consensus for AI ensembles and outline its advantages over traditional ensembling techniques in reducing nonfactual outputs. Preliminary considerations for implementation, evaluation criteria for convergence and accuracy, and potential challenges are discussed. The proposed mechanism demonstrates a promising direction for multi-agent AI systems to self-validate and deliver high-fidelity responses in complex tasks.
Launching a new blockchain system or application is frequently facilitated by a so called airdrop, where the system designer chooses a pre-existing set of potentially interested parties and allocates newly minted tokens to them with the expectation that they will participate in the system - such engagement, especially if it is of significant level, facilitates the system and raises its value and also the value of its newly minted token, hence benefiting the airdrop recipients. A number of challenging questions befuddle designers in this setting, such as how to choose the set of interested parties and how to allocate tokens to them. To address these considerations we put forward a game-theoretic model for such airdrop games. Our model can be used to guide the designer's choices based on the way the system's value depends on participation (modeled by a ''technology function'' in our framework) and the costs that participants incur. We identify both bad and good equilibria and identify the settings and the choices that can be made where the designer can influence the players towards good equilibria in an expedient manner.
Noor ul Misbah Khanum, Hayssam Dahrouj, Ramesh C. Bansal, Hissam Mouayad Tawfik
Microgrids have emerged as a pivotal solution in the quest for a sustainable and energy-efficient future. While microgrids offer numerous advantages, they are also prone to issues related to reliably forecasting renewable energy demand and production, protecting against cyberattacks, controlling operational costs, optimizing power flow, and regulating the performance of energy management systems (EMS). Tackling these energy management challenges is essential to facilitate microgrid applications and seamlessly incorporate renewable energy resources. Artificial intelligence (AI) has recently demonstrated immense potential for optimizing energy management in microgrids, providing efficient and reliable solutions. This paper highlights the combined benefits of enabling AI-based methodologies in the energy management systems of microgrids by examining the applicability and efficiency of AI-based EMS in achieving specific technical and economic objectives. The paper also points out several future research directions that promise to spearhead AI-driven EMS, namely the development of self-healing microgrids, integration with blockchain technology, use of Internet of things (IoT), and addressing interpretability, data privacy, scalability, and the prospects to generative AI in the context of future AI-based EMS.
Large Language Models (LLMs) demonstrate strong potential across a variety of tasks in communications and networking due to their advanced reasoning capabilities. However, because different LLMs have different model structures and are trained using distinct corpora and methods, they may offer varying optimization strategies for the same network issues. Moreover, the limitations of an individual LLM's training data, aggravated by the potential maliciousness of its hosting device, can result in responses with low confidence or even bias. To address these challenges, we propose a blockchain-enabled collaborative framework that connects multiple LLMs into a Trustworthy Multi-LLM Network (MultiLLMN). This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems. Specifically, we begin by reviewing related work and highlighting the limitations of existing LLMs in collaboration and trust, emphasizing the need for trustworthiness in LLM-based systems. We then introduce the workflow and design of the proposed Trustworthy MultiLLMN framework. Given the severity of False Base Station (FBS) attacks in B5G and 6G communication systems and the difficulty of addressing such threats through traditional modeling techniques, we present FBS defense as a case study to empirically validate the effectiveness of our approach. Finally, we outline promising future research directions in this emerging area.
Junhui Zhao, Yingxuan Guo, Longxia Liao, Dongming Wang
Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabilities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these challenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message-processing capabilities.
Vehicular Ad Hoc Networks (VANETs) are essential to intelligent transportation systems (ITS), enabling secure, real-time communication among vehicles and infrastructure. However, their decentralized and dynamic nature makes them vulnerable to threats such as Sybil attacks, message forgery, replay attacks, and Denial-of-Service (DoS). This paper presents VANETGuard, a lightweight scalable trust management system that enhances security and scalability in 5G-enabled smart vehicular networks. The proposed system integrates entropy-based anomaly detection, Bayesian inference for adaptive trust scoring, and a lightweight distributed ledger for decentralized, tamper-resistant trust storage. Large-scale simulations under realistic traffic and attack conditions demonstrate that VANETGuard achieves 99.97% detection accuracy, significantly reduces false positives, and maintains low latency and computational overhead while supporting over 300 vehicles. These results highlight VANETGuard’s potential to enable secure, efficient, and scalable trust mechanisms in next-generation ITS and urban mobility systems.
Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.
Market making serves as a cornerstone function in financial markets by ensuring continuous liquidity through the provision of bid and ask quotes across various asset classes. This scholarly examination traces the evolution of market making from its historical origins to its contemporary algorithmic manifestations, exploring the fundamental principles that govern spread mechanics, inventory management, and regulatory considerations. The analysis evaluates market makers' critical functions in liquidity provision, price discovery, volatility reduction, and transaction cost efficiency. Risk management strategies—encompassing hedging techniques, adverse selection mitigation, operational safeguards, and stress testing protocols—are examined in detail. Technological developments, particularly the rise of high-frequency trading, artificial intelligence applications, and decentralized finance models, have transformed market making practices while introducing new challenges for market resilience. The increasing complexity of market structures, coupled with evolving regulatory frameworks, continues to reshape market making strategies across traditional and emerging financial ecosystems.
In the context of the boom in blockchain technology since 2008, the range of applications for smart contracts, which were first introduced in 1995, has been expanding. Their development, however, has been hampered by legal issues. The legal research on smart contracts is of great significance. Theoretically, it challenges and enriches the traditional contract theory and legal system. From a practical point of view, it helps regulate its application and protect the rights of parties in various fields such as finance and supply chain. This article delves into the legal nature of smart contracts, analyzing their relevance to traditional contract elements such as offers and acceptance. It also discusses the protection difficulties such as the difficulty of contract modification, the difficulty of contract rescission and the difficulty of contract validity. Suggested solutions include incorporating it into the existing legal system and using soft law for regulation. In summary, although smart contracts face challenges, with the development of technology and the improvement of laws, their intelligent development prospects are broad, and will drive social innovation.
ABSTRACT Layer 2 blockchain solutions improve scalability and transaction efficiency while preserving the security and decentralization of base blockchains like Bitcoin and Ethereum. These solutions enable faster and more costeffective transactions, making them crucial for Decentralized Applications (DApps). This paper explores the fundamentals of Layer 2 technology, its various implementations, and its impact on blockchain ecosystems. It also examines the benefits and challenges of these solutions, emphasizing the different approaches adopted across blockchain networks. Layer 2 solutions address blockchain scalability issues by reducing the reliance on the main blockchain, which traditionally suffers from slow transaction speeds and high costs. Unlike Layer 1 solutions that alter the core blockchain infrastructure, Layer 2 solutions process most transactions offchain, using the main blockchain primarily for security and final settlements. By reducing congestion, Layer 2 solutions ensure smoother operation for blockchain applications, allowing them to reach their full potential in real-world use cases such as decentralized finance (DeFi), gaming, and enterprise solutions. Key Words: Layer 2 Blockchain Solutions, Blockchain Scalability, Decentralized Applications (DApps), Off-chain Transactions, Rollups (Optimistic and Zero-Knowledge), State Channels, Sidechains, Transaction Throughput, Latency Testing, Interoperability Issues, Security Vulnerabilities, Regulatory Compliance, Decentralization Risks, Smart Contract Security, Cross-chain Communication
Anniina Saari, Sarah Sinclair, Rebecca Leshinsky, Seppo Junnila
Digital transformation (DT) is complex and uncertain, particularly within traditional industries. Distributed ledger technology, such as blockchain, has driven DT across many global sectors. Despite its transformative potential, scaling blockchain solutions beyond pilot projects often fails due to technical, organisational and environmental barriers. Many failures stem from ineffective collaboration and strategic misalignment among ecosystem partners rather than from technology. This study examines collaborative processes and enabling capabilities required for successful DT by focusing on a longitudinal case study from Finland (2019–2023). The case follows a cross-industry, blockchain-driven DT initiative that achieved scale despite transitioning from private blockchain to centralised technologies after market entry. While blockchain's role in this success has been documented ( Saari et al., 2024 ), the ecosystem's collaboration mechanisms and strategies for tackling challenges remain unclear. Using the technology–organisation–environment (TOE) framework, this paper analyses how DT unfolded across these contexts, identifying best practice strategies and enabling capabilities. The study draws on 35 interviews with key stakeholders and thematic content analysis to identify critical scaling enablers. Our findings reveal that scaling blockchain-driven DT in cross-industry settings requires 1) industry-level mindset and shared vision, 2) formalised collaboration and public–private partnerships, 3) governance structure with a sustainable business model and funding, 4) complementary ecosystem roles and an early-stage champion, 5) stakeholder training and support and 6) flexibility in technology and agile implementation. These elements show that trust and strategic alignment can emerge even among direct competitors, offering valuable insights for enterprises exploring blockchain innovation in business-to-business contexts.
As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.
In the rapidly evolving landscape of digital finance, the increasing sophistication of fraudulent activities has created significant challenges for traditional detection systems. This research paper investigates the integration of federated learning with unsupervised deep learning techniques to meet the dual demands of data privacy and robust fraud detection. Using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset, we developed a federated framework based on deep autoencoders. The framework simulates decentralized model training across multiple financial nodes while ensuring that raw data remains local. The methodology includes detailed data pre-processing steps, the construction of a compact autoencoder architecture and a threshold-based approach to anomaly detection. Experimental outcomes demonstrate the model’s ability to distinguish between legitimate and fraudulent transactions by the use of performance evaluation through the use of Receiver Operating Characteristic (ROC) curves, confusion matrices, and reconstruction error distributions. Despite the challenges of class imbalance and data heterogeneity, the proposed model achieved promising results by maintaining competitive discrimination capabilities. Overall, the research study establishes the potential of federated learning combined with anomaly detection to provide scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments.