Ethereum enables the creation and execution of decentralized applications through smart contracts, that are compiled to Ethereum Virtual Machine (EVM) bytecode. Once deployed in the blockchain, the bytecode is immutable; hence, ensuring that smart contracts are bug-free before their deployment is of utmost importance. A crucial preliminary step for any effective static analysis of EVM bytecode is the extraction of the control-flow graph (CFG): this presents significant challenges due to potentially statically unknown jump destinations. In this paper we present a novel approach, based on Abstract Interpretation, aiming to build a sound CFG from EVM bytecode smart contracts. Our analysis, which is implemented in our static analyzer EVMLiSA, is based on a parametric abstract domain that approximates concrete execution stacks at each program point as an l -sized set of abstract stacks of maximal height h ; the results of the analysis are then used to resolve the jump destinations at jump nodes. Furthermore, EVMLiSA includes a checker for reentrancy detection, working on the constructed CFG. Our experiments show that, by fine-tuning the analysis parameters, EVMLiSA is able to build sound CFGs for all real-world smart contracts in the considered benchmark suite. Moreover, EVMLiSA successfully detects all reentrancy vulnerabilities in EVM bytecode smart contracts, while producing a small number of false positives.
The profound digital transformations currently shaping the worldâparticularly in the field of contractingâhave given rise to a new type of legal relationship known as self-executing smart contracts. These contracts are characterized by their autonomous conclusion and execution through blockchain technology, without the need for continuous human intervention. This poses a significant challenge to traditional legal frameworks, foremost among them the conflict-of-law rules in private international law. These rules presume the existence of certain criteria that allow for the determination of the law applicable to the legal relationship in dispute, whether based on the place of contract formation, the place of performance, or the nature of the contested relationship. However, the decentralized technical nature of self-executing contracts undermines these assumptions and weakens the ability of the adjudicator to apply traditional legal tools in understanding the relationship and attributing it to the appropriate legal system.
Blockchain has matured from being mainly linked with cryptocurrencies to being a central technology with revolutionary potential for financial systems globally. By allowing safe, decentralized, and tamper-resistant ledgers, blockchain can cut down on the cost of transactions, enhance transparency, and raise efficiency in many areas of finance. This paper discusses the applications of blockchain in payments, cross-border remittances, capital markets, trade finance, and compliance. It includes fresh data from international organizations, central banks, and private industry reports to note both Indian and global developments. For example, close to 91% of the central banks surveyed are now investigating central bank digital currencies (CBDCs), and India's pilot retail digital rupee has already signed up millions of customers. Concurrently, the World Bank also points out that the global remittance average cost still exceeds 4%, a far cry from policy levels, indicating that blockchain is able to bridge this gap. While the technology has potential for efficiency and financial inclusion, there are issues around interoperability, privacy, cyber threats, and regulatory clarity. The report concludes that the contribution of blockchain to finance will most likely be characterized not by substituting current systems, but by integrating programmability and transparency into the mainstream financial infrastructure.
The success of terrorist organizations in maintaining traditional resources to finance terrorist operations is sufficient to push them away from virtual currencies and their usual risks, as long as they are able to sell oil and transfer funds between their territories, and as long as their funds remain safe from attacks and persecution by the international community. Encrypted virtual currencies are characterized by high degrees of secrecy, privacy, and decentralization - and extremist religious groups And terrorism that adopts violence as a means of operation and expansion, and studying indicators indicating the growing importance of these currencies in circulation, exchange, and commercial transactions, nd in financing extremist religious groups and organizations, and financing the purchase of weapons and equipment used by these groups. It is a decentralized currency with no competent authority, and no central bank responsible for issuing it, and it is not subject to the restrictions of international banking and monetary institutions. This is a significant advantage that has attracted many individuals and groups to its circulation. Had international institutions and organizations been able to subject this currency to international oversight, or to a central authority, it would have lost its most important advantage, and terrorist and extremist organizations would have been unable to exploit it further.
Jingcheng Zhang, Yekai Zhou, Yingxuan Ren, Man Ho Au ¡ 9 authors
Advancements in sequencing technologies grant individuals unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate in privacy protection, limiting individuals' control over their genomic information, hindering data sharing, and posing challenges for biomedical research. Therefore, demand exists for an owner-governed system fulfilling owner authority, life cycle data encryption, and verifiability simultaneously. Here, we realized Governome, an owner-governed data management system empowering individuals with real-time control over their genomic data. Governome leverages a blockchain to manage transactions and permissions, granting data owners dynamic permission management with full transparency on data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form throughout its life cycle. Governome can support versatile genomic applications. We implemented and tested individual variant query, cohort study, genome-wide association study (GWAS) analysis, and forensics on 2,504 1000 Genomes Project (1kGP) genomes, demonstrating its robustness and scalability. Governome is open-source at https://github.com/HKU-BAL/Governome.
This systematic review examines the role of federated learning (FL) as a privacy-preserving paradigm for enterprise decision systems, synthesizing evidence from 187 peer-reviewed studies. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates algorithmic, systems, security, sectoral, and governance perspectives to provide a comprehensive account of current knowledge. Findings highlight that foundational algorithms such as FedAvg, FedProx, and SCAFFOLD dominate the methodological landscape, with significant adaptations emerging to address non-IID and unbalanced datasets across distributed organizational silos. Privacy-preserving mechanismsâincluding differential privacy, secure aggregation, homomorphic encryption, and multiparty computationâwere consistently applied as layered defenses, balancing mathematical guarantees with empirical resilience. The synthesis further revealed critical vulnerabilities to model poisoning, backdoor attacks, and gradient leakage, alongside defensive strategies such as robust aggregation, anomaly detection, and differential privacy clipping. Sector-specific implementations demonstrate FLâs practical utility in healthcare, finance, retail, logistics, telecommunications, and public services, where it enables collaborative modeling without violating data residency or confidentiality requirements. Governance and ethical frameworks, particularly GDPR, CCPA, and the NIST Privacy Framework, were found to shape deployment practices, while documentation artifacts such as datasheets, model cards, and privacy budget ledgers ensure accountability and transparency. Comparative surveys position FL as an integrative socio-technical architecture that unites distributed optimization, privacy engineering, adversarial robustness, and AI governance into a coherent enterprise-ready model. The review concludes that federated learning provides enterprises with a scalable, secure, and ethically aligned approach to leveraging distributed data while preserving trust and compliance.
Smart contracts are self-executing programs that run on blockchain platforms, most notably Ethereum.They automate transactions and enforce agreements without intermediaries, forming the foundation of decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized applications (dApps).Despite their growing importance, smart contracts remain prone to security vulnerabilities.Exploited bugs can lead to irreversible financial losses, service disruptions, and systemic failures.Although machine learningbased tools have emerged to aid vulnerability detection, two critical challenges remain: (1) limited fault localization at the function level, and (2) a lack of interpretable, human-readable explanations that enable developers to understand and fix issues effectively.This thesis addresses both challenges by proposing a unified framework that combines graph-based neural network modeling with explainable language model techniques.Specifically, the contributions consist of: (1) a function-level vulnerability detection system using Sub-Graph Neural Networks (Sub-GNNs), and (2) an explanation generation mechanism based on synthetic data and Chain-of-Thought (CoT) prompting using large language models (LLMs).These two components aim to improve both the technical granularity and practical usability of smart contract security analysis.The first part of the thesis introduces a novel function-level detection method that decomposes smart contracts into subgraphs centered around individual functions.While prior approaches using Graph Neural Networks (GNNs) operate at the contract level, they fail to pinpoint specific sources of vulnerabilities, limiting their value for debugging and remediation.To overcome this, we construct function-level subgraphs that incorporate controlflow and data-flow dependencies, preserving the semantic and structural context of each function.We then apply a Sub-GNN model to perform vulnerability classification at this finer granularity.Empirical evaluation on a curated synthetic dataset demonstrates that the proposed method achieves high precision in localizing faulty functions.Although it trades off a small margin of global classification accuracy compared to full-graph models, the localized predictions are significantly more actionable for developers.A benchmark comparison quantifies this trade-off and validates the effectiveness of subgraph-based analysis in practical settings.To facilitate this line of work, we develop a synthetic dataset of smart contracts with function-level vulnerability labels.The dataset includes diverse vulnerability types such as reentrancy, integer overflows, access control flaws, and unhandled exceptions.Each function is annotated with corresponding vulnerability types and contains metadata for constructing control and data flow graphs.This dataset fills a gap in the current landscape, which largely lacks fine-grained, labeled corpora for training and evaluating function-level detectors.The second component of the thesis tackles the issue of explanation.While detecting a vulnerability is important, understanding why it occurs and how to resolve it is crucial for real-world usability.Most existing detection tools output low-level indicators such as line numbers or vulnerability labels without offering semantic explanations.To address this gap, we propose an explanation generation system that produces structured, human-readable justifications for detected vulnerabilities.We construct another synthetic dataset where each entry consists of a vulnerable function, its formal label, and a professionally formatted explanation describing the issue, its cause, and suggested remediation steps.These explanations are derived from real-world audit patterns and follow a consistent template.Together, these two components form a comprehensive framework for smart contract vulnerability analysis.The Sub-GNN-based detector provides precise localization of faulty functions, while the CoT-guided explanation generator delivers semantic insight into the causes and consequences of the vulnerabilities.This dual capability bridges the gap between vulnerability detection and developer comprehension.The thesis concludes with a discussion of future directions.On the detection side, extending the Sub-GNN architecture to support inter-function and inter-contract reasoning could enable the modeling of call chains and complex compositional vulnerabilities.On the explanation side, integrating user feedback to iteratively refine generated explanations could support interactive auditing tools.Furthermore, we propose exploring multimodal models that combine graph-based embeddings with textual features to enhance both detection and explanation tasks.
Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.
With the shift from Centralized Finance (CeFi) to Decentralized Finance (DeFi), financial transactions have become trustless and self-executing through blockchain platforms, creating new opportunities while exposing the ecosystem to significant fraud risks. However, due to the lack of centralized oversight and the vulnerabilities in the blockchain platforms, DeFi transactions still face several security challenges, including fraud, identity theft, insider threats, and data breaches. Various methods, including regulatory frameworks, machine learning (ML), and deep learning (DL) techniques, are employed to detect these threats, particularly fraud, in DeFi transactions. Although these approaches help identify fraudulent activities, they face challenges related to accuracy and zero-day attacks due to insufficient data and the complexity of emergingfraud patterns. This study presents a novel approach for detecting and profiling fraud attacks, including zero-day ones in DeFi transactions, thereby eliminating the reliance on wallet transaction history, a limitation that previous research has heavily depended on. The proposed approach leverages two key components: a novel analyzer named DeFiTransLyzer (V1.0) and an Advanced Genetic Algorithm (AGA) for fraud transaction profiling. DeFiTransLyzer extracts 79 features from transaction and wallet data. At the same time, the AGA incorporates advanced techniques, including Penalized Fitness Evaluation, Elite Retention Strategy, Dynamic Mutation Rate, and dynamic generation, to create precise fraud profiles. By focusing solely on transaction features, the model ensures that all fraudulent activities, including zero-day ones, initiated within the first transaction of a new account can be effectively detected, without relying on prior wallet activity. To address the scarcity of comprehensive validation datasets, we introduce BCCCDeFiFraudTrans-2025, which comprises 1,026,867 annotated Ethereum transaction samples from the DeFi ecosystem. Additionally, the study establishes two taxonomies for systematic classification, covering the literature on fraud detection and profiling methods. Experimental results demonstrate that the proposed method achieves superior accuracy, precision, and efficiency while offering interpretability through its profiling mechanism. These promising outcomes highlight the potential of AGA profiling to enhance the detection and identification of fraudulent activities, including zero-day ones within DeFi transactions, contributing to the security and resilience of blockchainbased financial systems.
Jonas Lopes de Vilas Boas, Ygor S. Costa, Rodrigo da Rosa Righi, Antônio Marcos Alberti ¡ 5 authors
Reliable vaccine tracking and monitoring during transport and storage are essential to ensure dose effectiveness while minimizing waste. However, current solutions face challenges related to reliability, immutability, security, transparency, flexibility, extensibility, patient support, trust, and cost. Centralized systems are vulnerable to fraud, tampering, and manipulation, often relying on manual service contracts and lack of attested IoT devices to ensure data authenticity. Moreover, most existing platforms do not provide tamper-proof, near real-time monitoring, resulting in operational vulnerabilities and increased costs. This article presents Coldnet, a novel architecture for vaccine tracking and tracing that addresses these issues by: (i) integrating IoT device attestation with the registration of immutable data and flexible monitoring attributes; (ii) using Blockchain-based smart contracts to automatically manage tracking and monitoring clauses, improving security and enabling dynamic rule management; (iii) offering intuitive interfaces to support patient access to delivery information; and (iv) deploying an affordable, user-friendly IoT prototype to monitor and report vaccine status. A case study demonstrates Coldnet's feasibility, with an average delay of 20 seconds for recording and checking conditions â suitable for real operations. A simulation evaluating scalability and the impact of IoT attestation shows transaction costs of US$1.51 for ten vaccine batches with five monitored properties each, a cost deemed acceptable for the added features. Execution delays remained stable (0.85â0.92 seconds), with negligible impact from attestation. Coldnet contributes to reliable vaccine logistics, improving public health efforts by strengthening trust, transparency, and data integrity in vaccination campaigns.
Este trabalho apresenta uma metodologia de detecção de contratos inteligentes do tipo mixers na rede Ethereum. Utilizou-se um modelo de aprendizado de måquina baseado em Random Forest, treinado com transaçþes do Tornado Cash e balanceado com amostras de 100 endereços aleatórios não relacionados a mixers. O modelo foi treinado com dados de março de 2025 e validado em 29/10/2020, dia de alto volume de transaçþes, identificando corretamente 3 endereços do Tornado Cash.
The construction industry in developing countries continues to face significant challenges due to reliance on traditional, paper-based contract administration and management. This approach frequently results in inefficiencies, disputes, transparency issues and unethical practices. Although smart contracts enabled by blockchain technology present a promising solution to these longstanding issues, their adoption in developing countries remains limited. This study investigates the barriers to and strategies for the implementation of smart contracts within the construction industry, using Nigeria as a representative case. Adopting a qualitative research methodology, data were collected through semi-structured interviews with 14 experienced project managers selected via purposive sampling. A thematic analysis of the data identified several critical barriers, including resistance to change, low awareness, privacy concerns, legal uncertainties, technical hurdles, infrastructure deficits and economic instability. To overcome these barriers, the study proposes a strategic implementation framework informed by insights from interviewees and supported by literature. Key recommended strategies include educational and awareness initiatives, governmental support and policy reform, stakeholder collaboration, robust security measures, phased deployment and establishing supportive legal frameworks. The findings of this research offer valuable guidance for developing countries encountering similar constraints, providing a clear roadmap for successfully integrating smart contracts into construction practices.
This paper examines the legal status of Decentralized Autonomous Organizations (DAOs) within Macau's legal framework, with particular emphasis on the potential analogical application of Article 174 of the Commercial Code. Despite the absence of specific provisions addressing these novel blockchain-based entities, this research demonstrates that Macau's existing legal infrastructure possesses sufficient flexibility to accommodate DAOs through interpretive mechanisms. By analyzing the theoretical foundations of legal personhood, the distinctive characteristics of DAOs, and the underlying principles of Macau's commercial law system, this study proposes a viable pathway for recognizing DAOs as legitimate legal entities. The research reveals that while Article 174 was not originally conceived to address blockchain-based organizations, its purposive interpretation and analogical application could provide a provisional legal foundation for DAOs, pending more specific legislative developments. This approach not only addresses immediate practical concerns regarding the legal status of DAOs but also contributes to the broader discourse on legal adaptation to technological innovation in the commercial sphere.
Strengthening the economy through transformation in the tax system and decreasing the role of auditors and auditor-centric approaches should be among the priorities of the Iranian National Tax Administration.Given the country's urgent need to increase revenue sources to compensate for budget deficits, improving the tax collection system becomes even more crucial.According to clause (b) of article (1) of the "Law on Sales Terminals and Taxpayer Systems," blockchain technology can also be considered a type of sales terminal.One of the key features of blockchain is the enhancement of security, transparency, and efficiency.This study aimed to consider reality as closely as possible.For data collection, a library research method has been employed.It appears that a private consortium blockchain is a suitable option for the tax system.Based on the conducted reviews, there is still no definitive consensus mechanism for a tax system.Therefore, the proposed approach in this study is the use of a hybrid consensus mechanism, combining proof-of-authority and delegated proof-of-stake, which would be ideal for a blockchain-based tax system.One of the main features of this model is the use of multi-layered validation.A blockchain-based tax system designed to record all transactions and events related to invoice-based taxes should fundamentally be established on a multi-party smart contract between the buyer, seller, tax authorities of the origin and destination, the buyer's bank, and the seller's bank.To ensure the successful implementation of blockchain, several key considerations must be taken into account.
Blockchain smart contracts are pivotal to decentralized applications, yet their security remains a critical challenge. This study analyzes a dataset of 1,000 smart contracts to investigate known vulnerabilities, audit practices, and exploit patterns. The results reveal that audited contracts are significantly less prone to exploitation, with 75% exhibiting no exploit history compared to 55% of non-audited contracts. "Integer Overflow" and "Unchecked Call" were identified as the most prevalent vulnerabilities, contributing to 60% and 50% exploit rates, respectively. The study highlights the importance of transparent audit reporting, as contracts without available reports were exploited in 35% of cases. Additionally, hidden vulnerabilities in ostensibly secure contracts underscore the evolving sophistication of blockchain threats. This research emphasizes the need for robust security practices, including stricter coding standards, comprehensive audits, and advanced vulnerability detection techniques such as formal verification and machine learning. Future works aim to integrate security tools into development workflows and foster industry-wide collaboration to standardize auditing practices, thereby enhancing the security and trustworthiness of blockchain ecosystems.
Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereumâs gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
This study investigates the relationship between gas consumption and value transferred in Ethereum smart contracts, offering insights into resource utilization and efficiency within the blockchain ecosystem. Analyzing a dataset of 1,000 smart contracts, a moderate positive correlation r=0.45,p<0.05 was observed, indicating that higher gas consumption generally corresponds to larger financial transactions. The average gas consumption per contract was found to be 58,451,329.47 units, with a standard deviation of 20,123,456.89, highlighting significant variability in computational resource usage. Similarly, the average value transferred was 7,851.47 ETH, ranging from 0.001 ETH to over 100,000 ETH, showcasing the diverse financial applications of smart contracts. Efficiency analysis, measured as the ratio of value transferred to gas consumed, revealed an average efficiency of 0.00013 ETH per unit of gas, with some contracts achieving up to 0.01 ETH per unit of gas and others as low as 0.000007 ETH per unit of gas, reflecting varying levels of optimization. Outliers with disproportionately high gas consumption relative to value transferred were identified, suggesting inefficiencies or unique use cases. These findings underscore the importance of optimizing smart contract design to minimize gas costs and improve performance. Future research directions include functionality-specific analyses, anomaly detection, comparative studies across blockchain platforms, and exploring the economic implications of gas consumption. This work provides actionable insights for developers, researchers, and policymakers aiming to enhance the efficiency and sustainability of decentralized systems.
Ethereum's transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism has significantly altered the networkâs block generation process and transaction efficiency. This study investigates the impact of stake-based block generation on Ethereumâs transaction fees, block density, and overall network performance by analyzing a dataset containing 303 records of Ethereum blockchain activity. The findings reveal a strong positive correlation between block generation rate and stake reward (r = 0.78, p < 0.01) and coin stake (r = 0.74, p < 0.01), indicating that validators with larger stakes generate blocks more frequently. Additionally, transaction fees positively correlate with block density (r = 0.65, p < 0.01), suggesting that network congestion remains a key determinant of transaction costs, despite the PoS transition. Further analysis shows that Ethereumâs PoS system optimizes block space utilization, with an observed mean block density of 1393.6% and a transaction fee standard deviation of 0.12 ETH, demonstrating a more stable fee structure than PoW. The average transaction fee recorded is 0.179 ETH, with a maximum observed fee of 0.98 ETH and a minimum of 0 ETH in some cases. While PoS provides greater fee stability, minor fluctuations in fees persist due to congestion-related effects. Additionally, the mean stake reward is 0.98, suggesting a relatively stable staking incentive structure across different blocks.
This paper analyzes the risks posed by future-state quantum computers, specifically the âharvest now decrypt laterâ (HNDL) risk. We review foundational concepts of quantum computing to address the present and ongoing threat of HNDL to currently protected data. We use the Bitcoin network as an illustrative example to study the implications of HNDL for distributed ledger cryptocurrency networks that rely upon traditional cryptography. We posit that while cryptocurrency distributed ledger network maintainers could successfully deploy post-quantum cryptography (PQC) mitigations to protect the networkâs security and data integrity against a future-state quantum computer, data privacy of the networkâs previously recorded transactions remains vulnerable against a future-state quantum computer due to HNDL. The difficulty in protecting data privacy lies in the risk that a bad actor can obtain a distributed ledger replica, harvest the data, and in the fullness of time reveal previously obfuscated and confidential data using a sufficiently powerful quantum computer. The authors highlight this gap in data privacy protection and note the shortage of mitigations for the data privacy risks associated with the HNDL threat within distributed ledger networks.
The unprecedented rise of Bitcoin has marked a significant milestone in the evolution of decentralized finance (DeFi). Despite Bitcoin's groundbreaking contributions, it faces inherent challenges due to its reliance on the Unspent Transaction Output (UTXO) model, which limits its capabilities in executing complex transactions and embedding diverse data types. To overcome these limitations, Ordinals and Inscriptions have been introduced, allowing extensive data and information embedding within Bitcoin transactions. Building upon these advancements, the recent development of the BRC-20 standard leverages these capabilities to introduce fungible tokens on the Bitcoin network. However, these innovations have yet to be fully integrated into the broader blockchain and DeFi ecosystems, and they still face challenges related to interoperability, scalability, and user perception. In this paper, we explore the intricacies of Ordinal, Inscription, and BRC protocols to address these challenges by analyzing their functionalities, operational methodologies, and potential applications. We offer a detailed examination of the challenges and future prospects, shedding light on the unexplored potential of these technologies in transforming Bitcoin transactions and expanding its role in the DeFi space. By thoroughly analyzing these new developments, we aim to bridge the gap in current academic research and offer valuable insights for developers, investors, and enthusiasts. This paper serves as a foundation for future innovations, paving the way for more robust, scalable, and user-friendly applications in the DeFi and Web3 landscape.
Blockchain technology is emerging as a transformative force in the banking industry, offering decentralized, secure, and transparent solutions for financial transactions and operations. By eliminating intermediaries and enabling real-time data sharing across distributed ledgers, blockchain enhances efficiency in processes such as cross-border payments, smart contracts, KYC, and fraud prevention. Major banks and financial institutions are increasingly adopting blockchain to reduce costs and improve trust. However, challenges such as scalability, data privacy, lack of standardization, and integration with legacy systems hinder its full-scale deployment. Furthermore, global regulatory uncertainties and varying compliance requirements pose additional barriers. As regulators and central banks explore frameworks like Central Bank Digital Currencies (CBDCs) and regulatory sandboxes, a balanced approach is essential to foster innovation while ensuring security and consumer protection. This paper analyzes the key opportunities, challenges, and evolving regulatory landscape of blockchain in banking, highlighting its potential to reshape the financial ecosystem
Maria Dona Kristina Wati, Ainul Azizah, Fanny Tanuwijaya
The rapid development of digital technology has transformed the global financial landscape with the emergence of virtual currencies such as Bitcoin. As a decentralized digital asset based on blockchain technology, Bitcoin enables fast, anonymous, and cross-border transactions without central authority intervention. While these features offer convenience, they also pose risks of abuse in illegal activities, including narcotics transactions. In Indonesia, the use of Bitcoin in drug-related offenses presents complex legal challenges due to the absence of explicit criminal regulations governing such usage. This study examines the criminal liability of narcotics offenders who utilize Bitcoin, focusing on three core issues: the juridical basis for punishment, the characteristics of punishment under Indonesiaâs legal system, and an ideal penal concept for the future. The research employs a normative juridical method with statutory, conceptual, and case study approaches. Findings indicate that punishment remains based on Law No. 35 of 2009 on Narcotics, with Bitcoin regarded as a means rather than a core element of the crime. Although it does not constitute a new offense, its use may aggravate punishment through individualized sentencing. Sanctions involving digital assets remain suboptimal due to regulatory gaps. Therefore, this study recommends revising criminal laws to explicitly regulate Bitcoin use in drug crimes, developing sentencing guidelines that consider technological factors, and formulating responsive penal policies, including asset seizure and inter-agency collaboration through blockchain forensic technology.
Ejiroghene Amanda Onohwakpo, Ugochi Linda Onyeanuforo
This study examined cryptocurrency adoption patterns across developing economies using comprehensive data from 2018-2021. Through analysis of 45 developing countries, we identify key determinants of crypto adoption including financial inclusion gaps, currency stability, regulatory frameworks, and technological infrastructure. Our findings revealed that countries with higher inflation rates, lower banking penetration, and more favourable regulatory environments demonstrate significantly higher cryptocurrency adoption rates. The study contributes to understanding digital finance evolution in emerging markets and provides policy implications for fostering inclusive financial systems.