Mario Rusev, Rafael Schmidt, Edward Lambe, Christian Schmieder · 5 authors
International organizations including the Bank for International Settlements (BIS) have adopted SDMx (The standard for Statistical Data and Metadata) as the standard for exchanging official statistics. Trust in published data is essential for evidence-based policymaking. This paper shows how binding each SDMx dataset to its source using blockchain technology can enhance confidence in official statistics. We present a proof of concept implemented on the XRP Ledger (XRPL) and contribute, as an integrated whole, (i) an SDMx-native canonicalization and per-< Series > hashing pipeline, (ii) a domain-separated Merkle aggregation scheme for batched anchoring, (iii) a self-contained, identity-bound verification artefact in which the SDMx message itself carries both the ordered Merkle leaves and a W3C Verifiable Credential signed by a publisher identity key cryptographically bound to the publisher’s XRPL address via an on-chain attestation registry, so any consumer can re-derive the anchored root and verify the publisher’s identity from the file alone plus a single ledger lookup, (iv) an open-source XRPL-based reference implementation, and (v) a cost model that captures the batch-size / latency / fee trade-off and is solved for an economically optimal batch size. The system enables near-real-time data verification, provides cryptographic integrity guarantees, and establishes a foundation for future extensions, including zero-knowledge proofs and automated verification by AI agents. Measurements on the prototype show median publication latency of 3–5 s and verification latency of 1–2 s under the controlled test conditions described in Section 7. The approach is data-format-agnostic and can be extended to other structured statistical or regulatory formats.
The advent of fault-tolerant quantum computing represents the most significant and schedulable threat to the cryptographic foundations of blockchain infrastructure. Over $3.2 trillion in digital assets are currently secured by RSA, Elliptic Curve Cryptography (ECC), and ECDSA: algorithms provably broken by Shor's algorithm running on a Cryptographically Relevant Quantum Computer (CRQC). The Harvest Now, Decrypt Later (HNDL) threat means this risk is not future-dated. Adversaries with archival capability are already harvesting public blockchain data for retrospective decryption. In August 2024, NIST published three finalized post-quantum cryptographic standards: FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA). In 2025, NIST standardized HQC, providing code-based cryptographic diversity alongside the lattice-based primary algorithms. These standards are mandated for U.S. national security systems under NSA CNSA 2.0 and for high-risk sector operators in the EU under the EU PQC Roadmap. This paper introduces QubitChain.io: a natively quantum-safe Layer 1 blockchain implementing all four NIST post-quantum standards from genesis block. The protocol employs hardware Quantum Random Number Generator (QRNG) entropy at both key generation and consensus randomness levels, and introduces Proof of Quantum Entropy (PoQE), a novel consensus mechanism whose validator selection cannot be predicted or manipulated by any adversary regardless of computational capability. The paper provides the complete technical, economic, and governance specification for the QubitChain.io protocol, covering cryptographic architecture, QRNG system design, consensus mechanism, network protocol, tokenomics, governance, and regulatory compliance.
Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat
Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.
Verifiable Random Functions (VRFs) are cryptographic primitives that generate unpredictable randomness together with a publicly verifiable proof of correct generation following the protocol, a critical requirement for decentralized applications in blockchain infrastructure, decentralized finance, and online gaming. While distributed VRFs (DVRFs) eliminate dependence on a single trusted authority, existing constructions face a fundamental dilemma: linear proof sizes in the threshold parameter (DDH-DVRF) or reliance on computationally expensive bilinear pairings (GLOW-DVRF, FlexiRand). This thesis resolves both facets of this dilemma with three contributions despite the downside of adding another interaction round amongst the parties involved in the generation. We first introduce DVRFwCP, a distributed VRF with constant-size, pairing-free proofs achieved by layering a threshold structure over a Chaum-Pedersen NIZK system, where we use an augmented secure distributed key generation to produce the required nonce. We then introduce Icy-DVRF, which eliminates the quadratic interaction bottleneck of DVRFwCP by integrating the FROST-style preprocessed nonces, reducing total per-evaluation communication from O(n^2 t) to O(t) while preserving constant-size proofs. Finally, we introduce IcyVeil, the first pairing-free output-private DVRF, which extends Icy-DVRF with a Schnorr-based blinding mechanism that conceals the VRF output until revealed by the user. We evaluate all three constructions theoretically and empirically via Solidity smart-contract implementation on the Ethereum Sepolia testnet. The measurements show a 43.02% reduction in on-chain verification gas cost compared to GLOW-DVRF.
This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.
Oscar M. Bedoya, Jeferson Arango‐López, Jorge Hochstetter
The management of intellectual property (IP) agreements in universities continues to rely on static legal documents that are signed, archived, and consulted when necessary, but whose content is rarely formalized to facilitate their operation and verification. Consequently, obligations, permissions, restrictions, deadlines, scopes, and exceptions often remain scattered across clauses drafted in natural language, annexes, emails, and different document versions, which hinders their monitoring and makes compliance review dependent on intensive legal and administrative work. In response to this limitation, this article proposes an ontology to formalize non-disclosure agreements (NDAs) at the University of Caldas, Colombia, understood as a specific case within the broader management of IP agreements. The proposal adopts a modular Semantic Web architecture composed of a reusable ontological core and a specialized profile for NDAs. Its construction followed the METHONTOLOGY methodology, and its specification was supported by Competency Questions (CQs), which were subsequently translated into SHACL constraints and SPARQL queries. In addition, a SKOS vocabulary is incorporated to normalize synonyms and terminological variants typical of legal drafting in Spanish, together with a lightweight weak supervision layer based on regular expressions, SKOS, and structural signals to support clause labeling and the batch generation of RDF instances. Thus, the proposal enables querying, traceability, and verification over NDA content, while offering a formal basis for progressing toward automatable controls and their eventual articulation with smart contracts.
Diana Bonilla Guzmán, Sofía de las Nieves García Gámez, Rubén Mora-Ruano, Alvaro-Antonio Salas-Suárez
This study aims to identify the extent to which a country's level of governance implicitly determines and encourages the use of cryptocurrencies, and the main elements associated with the use of alternative currencies to traditional ones. The methodology used is a descriptive analysis of the variables, an econometric analysis through an ANOVA, and the application of a truncated regression model, which aims to bring the research closer to the possible correlation between governance indicators and the rate of adoption of cryptocurrencies. The study concludes that countries with low levels of governance are directly related to the greater adoption of cryptocurrencies. To the best of our knowledge, this study is the first to analyse the relationship between cryptocurrency adoption and institutional governance by comparing two regions with different levels of development. The research is limited by the existence of other factors that influence the analytical framework of cryptocurrency adoption, but the availability of data has allowed the present study to focus on governance aspects. Now, despite the fact that the governance indicators present a global analysis in terms of their measurement, the relevant aspects of each country are not specified. The adoption of cryptocurrencies in some countries may not be strongly related to governance aspects but rather to the friendly regulations that have been implemented.
This study investigates how economic policy uncertainty (EPU) innovations shape the daily returns of major cryptocurrencies, namely ADA, USDT, ETH, USDC, BTC, BCH, XRP, BNB, DOGE, and LTC. Using daily data from 07/06/2020 to 01/01/2026, the study applies symmetric and asymmetric wavelet quantile regression to capture state dependence across the conditional return distribution and horizon dependence across short-, medium-, and long-run components. The symmetric results reveal that the EPU—cryptocurrency nexus is heterogeneous, time-varying, and strongly dependent on both investment horizon and return quantile. In the short term, EPU generally has weak or insignificant effects across most cryptocurrencies. However, the medium-term results show stronger and more diverse responses, with ADA, LTC, DOGE, USDC, and BNB displaying positive effects at extreme lower and higher quantiles, while negative effects are mostly concentrated around middle quantiles. Conversely, BCH, USDT, ETH, and BTC exhibit stronger negative medium-term responses across most quantiles. In the long term, EPU mainly exerts adverse effects on ADA, LTC, DOGE, USDC, BNB, ETH, and BTC. The asymmetric findings further confirm that positive and negative EPU shocks transmit differently into cryptocurrency returns. Positive EPU shocks often generate negative medium- or long-term effects, whereas negative shocks frequently produce positive medium-term responses, particularly for LTC, DOGE, BCH, BNB, XRP, and USDT. Based on these findings, policy recommendations are proposed.
Traditional distributed systems theory has long encoded hard forks as a signof consensus rupture and governance failure. This paper proposes an alternativeanalytical framework: in the practice of decentralized governance, a hard fork isnot a system malfunction but a structural mechanism through which incommensurable cognitive architectures achieve legitimate evolution via the separation ofconceptual space when a dispute touches upon the fundamental commitments ofthe protocol. The paper first redefines a fork as a jump of the authority to modify rules across governance levels—a soft fork adjusts parameters within existingconstraints, while a hard fork alters the boundaries of the constraints themselves,constituting a “dimensionality lift” operation in governance space. Second, it distinguishes three normative types of forks—consensual, controversial, and cognitivelyincommensurable—and argues that only the third type reaches the governancelimits of soft forks. Using the 2015–2017 Bitcoin block size war and the 2016 TheDAOincident as core cases, the paper reveals the internal dynamics through whicha controversial fork evolves from a parameter dispute into framework incommensurability, and how an extreme semantic crisis forces a community to confrontthe tension between code rules and substantive justice. Based on this analysis, thepaper proposes three normative criteria for fork legitimacy—feedback anchoring integrity, cross-verification operability, and conceptual-space appropriateness—andargues that forks, as an “exit-separation” mechanism, possess a meta-governancefunction in decentralized governance analogous to the right of exit in traditionalpolitical theory.
This study examines the short-run effects of U.S. monetary policy shocks on cryptocurrency returns and asks whether digital assets respond to conventional macroeconomic transmission mechanisms. Focusing on the post-2020 period, it evaluates the magnitude, direction, and persistence of Federal Reserve rate shocks across Bitcoin, Ethereum, Solana, Ripple, and TRON. The analysis applies an SVAR-X framework to daily data for January 2020-December 2025. Cryptocurrency log returns are treated as endogenous variables, while the U.S. Dollar Index and VIX are included as exogenous controls; federal funds rate changes are modelled as strictly exogenous policy shocks. Impulse-response results show positive and significant contemporaneous responses for Bitcoin, Ethereum, Solana, and TRON, but no significant reaction for XRP. These effects dissipate within days, indicating modest, short-lived, and heterogeneous monetary-policy transmission rather than persistent effects on cryptocurrency return dynamics over time.
This study proposes a hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices. Hourly BTC/USD market data spanning June 2021 to November 2025 were combined with approximately 326,000 Bitcoin-related news headlines published over the same period. Sentiment scores in the range of [-1, +1] were generated for each headline using FinBERT, a transformer-based language model trained on financial texts, and were subsequently integrated with technical indicators such as trading volume, MACD, and RSI. The resulting combined feature set was modeled using an LSTM network to capture temporal dependencies. Empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics. The hourly hybrid model achieved the best performance, with an RMSE of 1,009 USD and an R² of 99.23%. Furthermore, a 30-day out-of-sample real-time evaluation yielded an RMSE of 941 USD. The consistency between in-sample and out-of-sample results indicates that the proposed framework maintains stable predictive performance over time.
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
The rapid growth of Real World Asset (RWA) tokenization faces a critical vulnerability: the "Physical Oracle Problem." While blockchain ensures digital immutability, it remains blind to the physical state of the underlying asset (e.g., structural degradation in real estate or hidden damage in naval vessels). This document introduces the Prop Trust Verified Standard (PTVS), a comprehensive forensic methodology designed to bridge this gap. Developed by Aurema Group, PTVS establishes a rigorous protocol for physical asset auditing, combining certified judicial expertise (Perito Judicial) with cryptographic anchoring. The methodology ensures that physical inspections, material verifications, and compliance checks are immutably recorded and linked to smart contracts (e.g., ERC-3643), providing institutional-grade trust for Family Offices, tokenization platforms, and regulatory bodies under frameworks like eIDAS (EU 910/2014). This report outlines the core principles, verification workflows, and case study applications of PTVS in real estate and maritime sectors. Español: El rápido crecimiento de la tokenización de Activos del Mundo Real (RWA) enfrenta una vulnerabilidad crítica: el "Problema del Oráculo Físico". Mientras que la blockchain garantiza la inmutabilidad digital, permanece ciega al estado físico del activo subyacente (ej. degradación estructural en inmuebles o daños ocultos en embarcaciones). Este documento presenta el Estándar Prop Trust Verified (PTVS), una metodología forense integral diseñada para resolver esta brecha. Desarrollado por Aurema Group, PTVS establece un protocolo riguroso de auditoría física de activos, combinando la pericia judicial certificada con el anclaje criptográfico. La metodología garantiza que las inspecciones físicas, verificaciones de materiales y controles de cumplimiento se registren de forma inmutable y se vinculen a contratos inteligentes (ej. ERC-3643), proporcionando confianza de grado institucional para Family Offices, plataformas de tokenización y organismos reguladores bajo marcos como eIDAS (UE 910/2014). Este informe detalla los principios fundamentales, flujos de trabajo de verificación y aplicaciones prácticas de PTVS en los sectores inmobiliario y naval.
The increasing adoption of cryptocurrencies has created new opportunities for digital financial innovation while simultaneously exposing individuals and institutions to sophisticated forms of financial fraud. Conventional rule-based fraud detection systems have become inadequate in addressing the dynamic and complex nature of blockchain-enabled financial crimes, leading to growing interest in the application of artificial intelligence (AI). This study systematically reviews the literature on artificial intelligence techniques for cryptocurrency fraud detection, with particular emphasis on their relevance to the Kenyan digital financial ecosystem. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Peer-reviewed studies published between 2020 and 2026 were identified from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Following the screening and eligibility assessment, 19 studies were included in the final qualitative synthesis. The findings reveal that machine learning, deep learning, hybrid AI models, and blockchain analytics significantly enhance cryptocurrency fraud detection by improving anomaly detection, transaction monitoring, predictive accuracy, and anti-money laundering compliance. Compared with traditional rule-based approaches, AI techniques provide faster, more adaptive, and scalable solutions capable of detecting evolving fraud patterns in decentralized financial systems. However, the review also identifies challenges relating to limited high-quality datasets, algorithmic bias, lack of explainability, cybersecurity risks, privacy concerns, and inadequate regulatory frameworks, particularly within developing economies. Furthermore, the review highlights a scarcity of empirical research focusing on cryptocurrency fraud detection in Kenya and identifies opportunities for developing localized datasets, explainable AI models, and context-specific regulatory frameworks. The study concludes that artificial intelligence has considerable potential to strengthen cryptocurrency fraud detection and financial security in Kenya, provided that technological, ethical, and regulatory challenges are adequately addressed. The findings provide valuable insights for researchers, financial institutions, technology developers, and policymakers seeking to enhance AI-driven fraud prevention within the country's evolving digital financial ecosystem.
E-Healthcare Systems (EHS) are transforming medical service delivery by enabling real-time data sharing, remote diagnostics, and integrated care via IoT and cloud infrastructures.However, the increasing volume of sensitive medical data being transmitted over distributed systems creates serious privacy and security concerns.This article reviews several papers on the EHS Data Privacy Framework, addressing critical issues such as illegal data access, identity exposure, and data integrity breaches.This review examines the existing data privacy frameworks used in EHS, focusing on four domains: traditional EHS, cloud-based EHS, IoT-based EHS and blockchain-based EHS with an emphasis on author, year, objective, and limitation.This framework provides a scalable and interoperable approach to protecting privacy for future healthcare systems.
Cryptocurrency has emerged as one of the most significant developments to accompany the digitization of global finance, and its footprint in India has expanded rapidly despite an unsettled regulatory environment. This paper examines how Indian investors perceive the opportunities and risks associated with cryptocurrency and blockchain technology, and evaluates whether their level of awareness shapes that perception. A structured questionnaire survey was administered to 158 respondents drawn from different age groups, educational backgrounds, occupations, and income levels in Karnataka, and the resulting data were analyzed using percentage analysis, frequency distribution, and the Chi-square test of independence. The findings indicate that a large majority of respondents, particularly those aged 21-30, view cryptocurrency and blockchain as tools capable of improving transparency, financial inclusion, and entrepreneurship, while simultaneously expressing concern over price volatility, cybersecurity threats, and unclear taxation rules. The Chi-square test confirmed a statistically significant association between investor awareness and perception of cryptocurrency (calculated value 19.41 against a critical value of 9.488 at 4 degrees of freedom and the 5 percent level of significance), leading to rejection of the null hypothesis. The study concludes that a clear, balanced regulatory framework combined with investor-education initiatives would allow India to capture the innovation potential of digital assets while containing the risks associated with their adoption.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
A Decentralized Application (DApp) is a distributed, open-source software application that runs on a peer-to-peer (P2P) blockchain network. DApps are emerging as a transformative force across various sectors, leveraging blockchain technology to create applications that operate autonomously, offer enhanced security and transparency, and function without a central authority. Common security issues with DApps include smart contract and blockchain vulnerabilities, phishing and social engineering attacks, and key management challenges. Mitigation strategies involve thorough code audits, formal verification, multi-signature wallets, and robust security frameworks. This chapter provides a detailed analysis of DApp security, focusing on theoretical aspects, architectural components, and specific vulnerabilities in smart contracts, oracles, blockchain protocols, front-end interfaces, and cross-chain interoperability mechanisms.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.
Validation of a secure blockchain architecture's effectiveness and robustness may be achieved via a methodical process that involves comprehensive testing and implementation in real-world environments. The testing process includes analyzing requirements, setting up the environment, and conducting detailed evaluations. All aspects of the smart contract, from its logic and functionality to its defences against common attack vectors like Sybil and re-entrancy vulnerabilities, are evaluated in these reports. Performance testing under different loads and realistic network conditions is essential for assessing scalability, latency, and throughput, in addition to API and integration testing, which ensure that system components operate well together. System resilience to defects and hostile events is tracked, critical test cases are automated, and large-scale peer-to-peer networks are modelled to evaluate the framework's robustness further. Supply chain verification and clinical trial administration are two examples of real-world applications of blockchain technology that demonstrate its ability to secure sensitive activities on a large scale. These use cases also give light on the system's efficacy, data integrity, and anomaly detection capabilities. Verifying the scalability, security, and reliability of a blockchain architecture against real business objectives via integrated deployments and complicated testing methods is essential for a safe blockchain.