This chapter explores the intersection of cryptocurrency crime and artificial intelligence (AI), highlighting both the threats posed by AI-driven cybercriminal activities and the potential of AI-based countermeasures. Cybercriminals increasingly leverage AI for money laundering, fraud, market manipulation, ransomware attacks, and identity theft, exploiting vulnerabilities in smart contracts and decentralized finance (DeFi) platforms. Conversely, AI is a powerful tool for combating these threats, aiding in blockchain analysis, anomaly detection, and Anti-Money Laundering (AML) enforcement. Advanced machine learning models enhance Know Your Customer (KYC) protocols and enable predictive crime prevention by analyzing transactional patterns. Additionally, this chapter examines challenges such as regulatory loopholes, adversarial AI, and jurisdictional complexities. The future implications of AI in financial crime prevention, including the role of quantum computing and emerging financial technologies, are also discussed.
Elham Albaroudi, Moustafa Elbehairy, Mohammad Nour Eddin Al Hinnawi, Mohammad Hatamleh · 6 authors
As smart energy systems become central to national sustainability strategies, the issue of data sovereignty—the right of nations to govern data generated within their borders—has gained critical importance in the broader context of global digital governance and energy security. However, most existing AI systems lack built-in mechanisms for jurisdictional compliance and local control. This paper investigates how Artificial Intelligence (AI) can support data sovereignty in smart grid environments. Using a comparative multiple-case study approach—including Gaia-X, Microsoft EU Data Boundary, a decentralized energy pilot in India, and Saudi Arabia’s NEOM, which represents a sovereignty-by-design model aligned with Vision 2030—the study examines AI-enabled compliance mechanisms, federated learning, and sovereign cloud infrastructures. Expert interviews with stakeholders in policy, energy, and AI provide further context. Findings show that AI offers strong potential for enforcing sovereignty when supported by aligned legal frameworks and sovereignty-by-design architecture. For example, in India’s pilot project, federated AI reduced cross-border data transfers by more than 70% while maintaining forecasting accuracy. Beyond the energy sector, the proposed conceptual framework has applications in finance, healthcare, and smart cities. In particular, the NEOM case highlights Saudi Arabia’s leadership in embedding ethical and cultural governance into AI-enabled sovereignty. Practical recommendations are made to guide sustainable and ethical AI deployment in digital energy infrastructure. These results support global digital sovereignty goals and align with SDGs related to clean energy, innovation, and governance.
This study examines how Bitcoin’s relationships with major financial assets evolved following the January 2024 spot ETF approval. Using DCC-GARCH analysis, we find Bitcoin’s correlation with stocks exhibits a complete trend reversal from declining to increasing trends, while other assets show more limited changes. TVP-VAR spillover analysis reveals declining Bitcoin self-spillover and strengthened bidirectional risk transmission with equity markets. These findings indicate the ETF approval altered Bitcoin’s market role, transitioning from decoupling to integration with financial assets and reducing its diversification benefits in equity-focused portfolios.
The emergence of 6G networks enhances the speed and compatibility of Internet-of-Things (IoT) devices in vehicular ad hoc networks (VANETs), leveraging underutilized bands to improve wireless communication and security, though its adaptability may introduce cyber vulnerabilities; to address this, we propose an energy-efficient consortium-based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices, offering secure VANET control through a lattice-based ring signcryption scheme that ensures timely message relaying while preserving vehicle anonymity and cloud data confidentiality, with blockchain blocks formed via secure peer nodes and service provider data; our protocol’s security was rigorously validated through analysis and Python-based implementation, achieving 42.1 ms computational cost and 1026-bit communication overhead, and proving effectiveness across varying block and transaction loads, while guaranteeing key security properties-anonymity, linkable privacy, unforgeability, and confidentiality-even under quantum threats, using lattice-based cryptography, Zero-Knowledge Proofs (ZKP), and blockchain immutability.
Yan Watequlis Syaifudin, Daffa Cahyo Alghifari, Gunawan Budiprasetyo, N. Funabiki · 9 authors
Carbon credit trading is an evolving system that aims to mitigate climate change by providing financial incentives to reduce greenhouse gas emissions, where each carbon credit corresponds to the right to emit one ton of carbon dioxide or its equivalent. Indonesia’s carbon market, which leverages blue carbon credits from coastal ecosystems, is estimated to have an economic potential of approximately USD 25 billion between 2022 and 2026. Decentralized carbon credit trading leverages blockchain technology to create a transparent and secure marketplace that fosters peer-to-peer connections and democratizes access, utilizing features such as smart contracts and tokenization to enhance efficiency and reduce transaction costs. The study presents a model for a decentralized marketplace that incorporates a streamlined workflow, a blockchain framework, and automated processes via smart contracts, further enabling tokenization of carbon credits into ERC-20 tokens. However, implementing this system in Indonesia entails navigating regulatory compliance with environmental and financial laws while overcoming challenges such as interoperability with existing systems, fostering user adoption, and addressing market fragmentation to assure the integrity and success of the market.
Forecasting the price of bitcoin assets is a difficult task, especially as bitcoins are highly volatile and speculative. In this paper we leverage the non linear capability of deep and machine learning models to enhance bitcoin forecasts. We propose a systematic comparison of different deep learning and machine learning models, based on their Accuracy, Security and Explainability characteristics. The empirical findings reveal that, while CNN-GRU, GRU and LSTM are the most accurate models, for maximum cumulative return and risk adjusted performance GRU and CNN are preferred. Whereas, for transparent and stable decision-making, Random Forest and XGboost are a good choice and, for robustness, CNN and LSTM are the best choice. Ultimately, the choice of a model depends on the objectives of the analysis.
Real-World Assets (RWAs) serve as a bridge between traditional financial instruments and decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can extend the scope of decentralized finance. Industry forecasts further indicate rapid growth in tokenized RWAs after 2025, underscoring their potential role in the evolution of digital financial markets. However, in the current multi-chain environment, RWAs face challenges such as repeated authentication across multiple chains and inefficiencies arising from multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and cross-chain interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, this paper adopts a cross-chain channel that supports efficient RWA settlements, and we refine its design so that the channel does not need to be closed immediately after each settlement, thereby reducing on-chain cost. We implement the framework and evaluate its performance via simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
The relevance of the topic is due to the need to specify the circle of participants in the virtual asset market, since participants are an important element of legal relations in the virtual asset market. However, a clear circle of them has not yet been formed. With the development of the virtual asset market, new participants emerged, which explains the need to specify their circle at the current stage of market formation. The need for such specification is also due to the fact that the role and importance of the virtual asset market for the economy of Ukraine, including for its post-war recovery, require urgent certainty regarding the legal framework for the market functioning, which, in particular, should concern participants, which will allow to solve a problem of regulation of other legal aspects of the market functioning. The purpose of the article is to specify the circle of the virtual assets marker participants and to substantiate their place and role in this market by means of classification. Based on the provided study, author specifies the circle of virtual assets marker participants, which are proposed to be divided into the following functional groups: 1) the main participants – virtual assets service providers, which, depending on the activities carried out, may be business entities, the range of which is presented in Article 55 of the Commercial Code of Ukraine, business entities established under the laws of foreign states, as well as decentralized autonomous organizations functioning as virtual assets service providers; issuers (including miners); offerors; consumers; and individuals conducting transactions with virtual assets in their own interests; 2) participants with auxiliary functions that provide the necessary conditions for the functioning of the virtual asset market by providing services (banking, insurance, legal, consulting, etc.); 3) participants with special functions related to state regulation of the virtual asset market and self-regulatory organizations. The author suggests that the concept of “virtual asset market participant” should be properly enshrined in national legislation.
Introduction: The interplay between digital culture and non-fungible tokens (NFTs) represents a major paradigm shift in the creative industries, especially in the European context. This article examines the relevance of this issue, highlighting the transformative impact of NFTs on artistic practices and the art market in general. The study addresses the dual nature of NFT as a source of opportunities and challenges for artists, including issues of authenticity, intellectual property rights and environmental sustainability. The study aims to understand the implications of NFTs for European artists, focusing on their potential to enhance economic opportunities while recognizing the associated risks. Methodology: A mixed-methods approach is employed, including a comprehensive review of the academic literature, as well as a survey of artists, collectors and entrepreneurs to gather information on their experiences and perceptions of NFTs. Findings: These reveal that while NFTs offer innovative avenues for monetization and audience engagement, they also raise critical concerns about market volatility, regulatory frameworks and ethical implications. As artists grapple with the complexities of market volatility and ethical dilemmas, there is an imperative need for clear regulations and frameworks, as well as fostering knowledge and collaboration. Conclusions and discussion: This article contributes to the discourse on the future of digital culture in Lithuania, suggesting that a balanced approach is essential to harness the benefits of NFT while mitigating its challenges
O.O.O. Law firm, Upper Marlboro, USA, Oluwafunmibi Grace Ajakaye, Adeyinka Lawal, Independent Researcher, Texas, USA;
The emergence of blockchain technology and non-fungible tokens (NFTs) has fundamentally transformed the digital landscape, creating unprecedented challenges for intellectual property protection and copyright enforcement across transatlantic jurisdictions. This comprehensive study examines the evolving regulatory frameworks governing digital assets, blockchain-based intellectual property rights, and copyright infringement in the context of NFTs within both European Union and United States legal systems. The research investigates how traditional intellectual property laws are being adapted to address the unique characteristics of blockchain technology, including immutability, decentralization, and cross-border transactions that often transcend conventional jurisdictional boundaries. The study employs a comparative legal analysis methodology, examining recent legislative developments, judicial precedents, and regulatory guidance from key transatlantic jurisdictions including the United States, United Kingdom, Germany, France, and the European Union as a collective entity. Through systematic analysis of case law, regulatory frameworks, and emerging legal doctrines, this research identifies critical gaps in current legal protections and proposes innovative solutions for harmonizing intellectual property enforcement in the digital age. The analysis reveals significant disparities between European and American approaches to blockchain governance, with European jurisdictions typically favoring more prescriptive regulatory frameworks while American systems rely heavily on existing intellectual property doctrines adapted for digital contexts.
This paper proposes a hybrid access control system that integrates the usability of Web2 authentication (Google Login) with the transparency and integrity of Web3 technologies (blockchain and smart contracts). The system enables users to authenticate via their existing Google accounts without managing crypto wallets or private keys, while access permissions are securely recorded on-chain through smart contracts. To ensure cryptographic key security without relying on a centralized authority, the design incorporates Distributed Key Management (DKM). This approach addresses the challenge of balancing usability with verifiability in data access control. By embedding decentralized guarantees within a centralized web service interface, the system enables practical and transparent access control. The proposed architecture demonstrates the potential for a general-purpose, auditable module that facilitates user-consented data sharing with third parties.
Web3 has attracted considerable attention in fields including DeFi, DApps, and NFTs due to its decentralization, enhanced privacy, and user-centricity. However, interoperability and scalability challenges hinder its widespread adoption. While deploying anonymous credentials across Web3 networks to enable cross-network service access is a potential solution to these challenges, existing credential systems remain limited by centralized management, high energy consumption, and credential abuse, making them unsuitable for Web3 environments. To overcome these limitations, we propose a decentralized anonymous functional credential (DAFC) scheme that is efficient, privacy-preserving, and linkable. Unlike existing schemes, DAFC enables users to generate a single proof embedding attributes$x$for requesting services under different access policies. Each provider can use the functional key$sk_{F}$associated with their respective access policy$F$to extract$F(x)$for attribute verification. This significantly reduces authentication computational overhead. Furthermore, DAFC's linkability effectively mitigates credential abuse risks. As an additional contribution, we propose a novel construction of non-interactive zero-knowledge functional proof (fNIZK) based on one-out-of-many proofs and functional encryption for inner products, which is the building block of DAFC. Security analysis demonstrates that DAFC achieves anonymity, unforgeability, and linkability. Performance evaluation shows that DAFC outperforms prior schemes in both computational and communication overhead when requesting at least 6 services with distinct access policies.
This study investigates three blockchain-based initiatives empowering entrepreneurial communities in Africa, focusing on Cape Verde, Angola, and Nigeria. Utilizing a multiple case study approach, it explores the implementation of decentralized public Blockchain Technology (BT) and cryptocurrency platforms. These platforms, which operate on a proof-of-stake mechanism and are fully open source, aim to identify the characteristics of successful BT system implementation and the pivotal role of blockchain-aligned entrepreneurship. The findings underscore BT's precision and effectiveness in managing entrepreneurship programs, facilitating real-time adaptation, and decision-making to address social and economic disparities. The study highlights BT's capacity to enhance operational efficiency and align business models with strategic goals, necessitating diverse skill sets for effective implementation. This innovative research offers valuable insights into how blockchain can rapidly integrate management, leadership, and execution capabilities into actionable strategies, ultimately empowering African entrepreneurs and fostering inclusive community development.
This chapter delves into the revolutionary emergence of Decentralized Finance (DeFi), its potential to revolutionize, and its vulnerabilities. Based on blockchain technology, DeFi bypasses conventional financial intermediaries through smart contracts to execute lending, trading, and other economic activities. The permissionless aspect of DeFi increases financial inclusion worldwide, especially for the unbanked and underbanked. Yet, this openness also exposes DeFi platforms to threats such as vulnerabilities in smart contracts, oracle manipulation, flash loan attacks, and governance attacks. Case studies like the Poly Network hack, Mango Markets manipulation, and Squid Game token rug pull illustrate these risks. The research covers technology innovations such as smart contract audits, formal verification, decentralized oracles, and AI-based threat detection to strengthen DeFi's future. It also examines how regulatory sandboxes and decentralized identification solutions can balance innovation and regulation.
Escrow trust is a foundational requirement for high-value campaign execution in Web3 marketing marketplaces. When campaign budgets exceed USD 50,000 and settlement is enforced on-chain, the security properties of the escrow contract and its surrounding settlement architecture determine whether the platform can be trusted by enterprise brands. Naive escrow designs — single-key deployment, monolithic contract logic, and implicit state transitions — expose platforms to fund loss through key compromise, smart contract exploit, and fraudulent dispute resolution. This paper presents SESA (Secure Escrow and Settlement Architecture), a formal engineering framework for Web3 campaign escrow that integrates multi-signature approval policies, strict role segregation between campaign management and fund release authority, control-plane and data-plane separation with hardware-backed signing, and explicit finite-state machine governance of all escrow lifecycle transitions including dispute resolution. SESA is grounded in a formal threat model that enumerates eleven attack vectors specific to Web3 escrow systems and maps each to a corresponding architectural control. A formal verification of the escrow state machine using the TLA+ specification language demonstrates the absence of deadlock, fund loss, and unauthorised release under all reachable states. A gas cost analysis of the reference Solidity implementation demonstrates that SESA's security controls add a mean overhead of 23% in gas cost relative to a naive single-key escrow — a trade-off that enterprise buyers consistently accept in exchange for verifiable security assurances. SESA enables campaign budgets that would be commercially unviable under insecure escrow designs to flow safely through the platform, directly expanding the addressable market for high-value brand partnerships.
Alex Fernando Erazo-Luzuriaga, James Alberto Morales-Chincha
The expansion of digital financial systems in Latin America has increased the need for traceable, verifiable, reliable, and timely information to support managerial and financial decision-making. This study aimed to analyze the relationship between blockchain use, information transparency, and the quality of such decisions. A qualitative, documentary, non-experimental, and cross-sectional study was conducted through thematic analysis of scientific articles, technical reports, regulatory documents, and institutional reports published between 2021 and 2025. The results identified six application categories and more than 100 regional use cases across 23 countries, involving digital identity, payments, verifiable credentials, traceability, and digital assets. The findings also showed that traceability supported control, verifiability strengthened validation, information integrity enhanced consistency, and timeliness improved information availability for decision-making. The results indicated that these benefits depended on technological architecture, governance, interoperability, and data protection. It was concluded that blockchain can strengthen financial information transparency; however, it does not independently guarantee improvements in efficiency, profitability, or decision-making accuracy.
Weihao Zhu, Long Shi, Kang Wei, Zhen Mei · 7 authors
As an enabling architecture of Large Models (LMs), Mixture of Experts (MoE) has become prevalent thanks to its sparsely-gated mechanism, which lowers computational overhead while maintaining learning performance comparable to dense LMs. The essence of MoE lies in utilizing a group of neural networks (called experts) with each specializing in different types of tasks, along with a trainable gating network that selectively activates a subset of these experts to handle specific tasks. Traditional cloud-based MoE encounters challenges such as prolonged response latency, high bandwidth consumption, and data privacy leakage. To address these issues, researchers have proposed to deploy MoE over distributed edge networks. However, a key concern of distributed MoE frameworks is the lack of trust in data interactions among distributed experts without the surveillance of any trusted authority, and thereby prone to potential attacks such as data manipulation. In response to the security issues of traditional distributed MoE, we propose a blockchain-aided trustworthy MoE (B-MoE) framework that consists of three layers: the edge layer, the blockchain layer, and the storage layer. In this framework, the edge layer employs the activated experts downloaded from the storage layer to process the learning tasks, while the blockchain layer functions as a decentralized trustworthy network to trace, verify, and record the computational results of the experts from the edge layer. The experimental results demonstrate that B-MoE is more robust to data manipulation attacks than traditional distributed MoE during both the training and inference processes.
Prediction markets have gained adoption as on-chain mechanisms for aggregating information, with platforms such as Polymarket demonstrating demand for stablecoin-denominated markets. However, denominating in non-interest-bearing stablecoins introduces inefficiencies: participants face opportunity costs relative to the fiat risk-free rate, and Bitcoin holders in particular lose exposure to BTC appreciation when converting into stablecoins. This paper explores the case for prediction markets denominated in Bitcoin, treating BTC as a deflationary settlement asset analogous to gold under the classical gold standard. We analyse three methods of supplying liquidity to a newly created BTC-denominated prediction market: cross-market making against existing stablecoin venues, automated market making, and DeFi-based redirection of user trades. For each approach we evaluate execution mechanics, risks (slippage, exchange-rate risk, and liquidation risk), and capital efficiency. Our analysis shows that cross-market making provides the most user-friendly risk profile, though it requires active professional makers or platform-subsidised liquidity. DeFi redirection offers rapid bootstrapping and reuse of existing USDC liquidity, but exposes users to liquidation thresholds and exchange-rate volatility, reducing capital efficiency. Automated market making is simple to deploy but capital-inefficient and exposes liquidity providers to permanent loss. The results suggest that BTC-denominated prediction markets are feasible, but their success depends critically on the choice of liquidity provisioning mechanism and the trade-off between user safety and deployment convenience.
Namrata Marium Chacko, V G Narendra, Mamatha Balachandra, T Manoj
Blockchain technology has seen a rapid pace of development and expanded application domains swiftly due to the rising demand for decentralized trust, transparency, and integrity. The consensus algorithm plays a critical role in ensuring trust, immutability and governance of the decentralized network. However, traditional consensus face challenges such as high energy consumption, low scalability, security, and fault tolerance. Researchers have been investigating Lightweight Consensus to overcome these challenges. Lightweight Consensus is a mechanism that enables a more efficient and scalable blockchain system while ensuring security and immutability. This work uses the Systematic Literature Review method to comprehend Lightweight Consensus. 127 studies were grouped based on application specific network, and an in-depth analysis was done on the characteristics of the consensus. A novel taxonomy of Lightweight Consensus based on the agreement method and round propagation is proposed. Various parameters that needed consideration for a Lightweight Consensus are also analyzed. Finally, the study makes recommendations for future research on Lightweight Consensus in blockchain, emphasizing the importance of more empirical investigations and real-world implementations. This study offers a comprehensive overview of the current research landscape on lightweight consensus in blockchain, shedding light on its potential impact on the evolution of blockchain technology. It also serves as a valuable guide for researchers, helping them identify the most suitable consensus features for specific application domains with unique requirements.
This study examines the philosophical-legal foundations of smart contracts through the lens of transforming concepts of autonomy and determinism. The semantic gap between the natural language of law and the formal language of programming is investigated. The ontological status of smart contracts as hybrid sociotechnical phenomena is analyzed. A conceptual vision of "executable law" is proposed for understanding new forms of algorithmic normativity in the digital era.