Search Results (224)

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers. Full article

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures. Full article

Traditional Time-Stamping Authorities provide reliable temporal evidence. However, they operate as single points of trust and do not supply a tamper-evident record of event ordering. This paper presents a standards-compliant extension that anchors each issued timestamp token to a blockchain ledger while preserving full compatibility with existing TSA clients. Our proposal is compliant with RFC 3161. The implementation uses an identifier in the token that is also included in the distributed ledger. Experiments were conducted on the Ethereum and Hyperledger Fabric networks. Our design allows for external verification of the existence and relative ordering of tokens without modifying the RFC-defined validation process. Experimental evaluation compares issuance latency, anchoring time, and transaction cost across both networks. Our work presents a practical and viable approach to enhancing trust in digital signature infrastructures by combining the regulatory reliability of qualified TSAs with the auditability and persistence of distributed ledgers. Full article

Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs. Full article

Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications. Full article

The construction industry faces significant payment processing challenges characterized by delays, disputes, and cash flow constraints affecting contractors. Traditional systems rely on fragmented, paper-based processes lacking transparency and real-time integration between project progress and financial transactions. This paper proposes a decentralized application that integrates BIM 5D capabilities with Solana blockchain technology for automated construction payment processing, called DB5D. The framework consists of several components: a web-based 3D viewer utilizing Autodesk Forge for BIM visualization, construction schedule integration from planning software, Solana blockchain programs using Program-Derived Address (PDA) and Cross-Program Invocation (CPI) for secure payment processing, and decentralized document management through InterPlanetary File System (IPFS) with Content Addressable Archives (CAR) compression. The system enables direct linkage between measurable project progress and automated payments by allowing stakeholders to extract quantities from BIM models, record construction task completion with supporting documentation, and trigger blockchain-based token transfers upon client approval. Comprehensive validation involving construction industry professionals confirms the framework’s practical viability. It demonstrates significant improvements in payment transparency, administrative efficiency, and scalability compared to existing blockchain implementations, while enabling economically feasible micro-payments throughout project lifecycles. Full article

Metaverses have been hailed as the next arena for a wide spectrum of technovation and business opportunities. This research (∑ N = 714) focuses on the three underexplored areas of virtual commerce in AI-enabled metaverses: blockchain-powered cryptocurrencies, non-fungible tokens (NFTs), and AI-powered virtual influencers. Study 1 reports the mediating effects of (dis)trust in AI-enabled blockchain technologies and the moderating effects of consumers’ technopian perspectives in explaining the relationship between blockchain transparency perception and intention to use cryptocurrencies in AI-powered metaverses. Study 1 also reports the mediating effects of Neo-Luddism perspectives regarding metaverses and the moderating effects of consumers’ social phobia in explaining the relationship between AI-algorithm awareness and behavioral intention to engage with AI-powered virtual influencers in metaverses. Study 2 reports the serial mediating effects of general perception of NFT ownership and psychological ownership of NFTs as well as the moderating effects of the investment value of NFTs in explaining the relationship between acknowledgment of the nature of NFTs and intention to use NFTs in AI-enabled metaverses. Theoretical contributions to the literature on digital materiality and psychological ownership of blockchain/cryptocurrency-powered NFTs as emerging forms of digital consumption objects are discussed. Practical implications for NFT-based branding/entrepreneurship and creative industries in blockchain-enabled metaverses are provided. Full article

The design philosophy of a new smart gas meter is presented, based on an ultrasonic sensor employing LoRa and/or NB-IoT protocols and blockchain technologies to overcome the data integrity and security issues with a completely modular design. The architecture is organized into two separate blocks, the former for measurement and the latter for communication, and it presents original characteristics with respect to the state of the art. The accuracy of measured data is studied, paying attention to the fluid dynamic effects of the geometrical layout on the flow rate ultrasonic sensor and the environmental temperature and pressure for variable gas flow rate values. As for data security issues, the proposed solution is critically analyzed with reference to the data string organization and the procedure by which the data are stored and prepared for transmission into the blockchain. Finally, a local network of counters is designed and simulated in order to check the compliance of the provided hardware and software solutions with the predicted computational load. Full article

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis. Full article

Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token on the Tron blockchain, we propose a two-layer transaction network-based approach for virtual currency address identity recognition for digging out hidden relationships and encrypted assets. Specifically, a two-layer transaction network is constructed: Layer A describes the flow of USDT-TRC20 between on-chain addresses over time, while Layer B represents the flow of TRX between on-chain addresses over time. Subsequently, an identity metric is proposed to determine whether a pair of addresses belongs to the same user or group. Furthermore, transaction records are systematically acquired through blockchain explorers, and the efficacy of the proposed recognition method is empirically validated using dataset from the Key Laboratory of Digital Forensics. Finally, the transaction topology is visualized using Neo4j, providing a comprehensive and intuitive representation of the traced transaction pathways. Full article

Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities. Full article

As governments increasingly explore digital financial instruments to diversify funding channels and expand citizen participation, Thailand’s G-Token represents an early attempt to integrate blockchain technology into sovereign debt issuance. This study examines its potential implications through a multi-dimensional risk and governance framework, situating the analysis within both domestic regulatory structures and international benchmarks. The evaluation considers macroeconomic effects—such as potential shifts in monetary policy transmission, bank disintermediation risks, and systemic liquidity impacts—alongside micro-level concerns involving investor protection, market integrity, and financial literacy. Using comparative analysis with the European Union, Singapore, and United States regulatory approaches, the paper identifies critical gaps in legal classification, oversight maturity, and structural safeguards. Findings indicate that while Thailand’s design—particularly its separation from payment systems—supports monetary coherence, its ad hoc legal integration, reliance on administrative investor protections, and early-stage market infrastructure pose vulnerabilities if adoption scales. The study concludes that achieving long-term viability will require explicit statutory authorization, enhanced disclosure and governance standards, strengthened interagency oversight, and inclusive market access strategies. These insights provide a structured basis for emerging economies seeking to adopt government-backed tokenized instruments without undermining financial stability or public trust. Full article