Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SPs) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness. Although ADS can verify the existence of individual query results, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data search in blockchains and traditional database systems. Drawing upon these insights, we introduce BPI, a lightweight framework that enables efficient keyword queries and maintenance with low overhead. We propose "Articulated Search", a query pattern specifically designed for blockchain environments that enhances search efficiency while significantly reducing costs during data user updates. Furthermore, BPI employs a suite of validation models to ensure the inclusion of all valid content in search results while maintaining low overhead. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain, surpassing EthMB+ and state of the art search databases commonly used in mainstream hybrid storage blockchains (HSB).
T Buvaneswari, Mageshkumar Naarayanasamy Varadarajan, M. Mythily, Hemantha Kumar B N · 8 authors
The swift expansion of the Internet of Things (IoT) has expedited the implementation of smart sensors, generating substantial volumes of time-series data that require safe, efficient, and dependable management. Current centralized systems have constraints in maintaining integrity, protecting communications, and deriving economic value from this data. A blockchain-based smart house gateway network is suggested to address security concerns in smart home environments. The framework has three layers: device, gateway, and cloud. Blockchain technology is integrated at the gateway layer to provide decentralized storage and safe data interchange, eliminating single points of failure inherent in centralized systems. This integration ensures authentication, high availability, and secure communication across devices and stakeholders. The system utilizes Ethereum blockchain technology and is assessed based on important parameters such as response speed and detection accuracy. Experimental study demonstrates that the framework much surpasses traditional methods, improving resilience and reliability in smart home IoT ecosystems. The suggested system offers a scalable approach for safe data management and dependable value exchange in dispersed settings.
Suhasini Subba Rao, M. Pavan, G. Sai Venkat, Pavan Kumara Kasam Shiva · 6 authors
Decentralized Finance (DeFi) is revolutionizing financial systems by eliminating intermediaries and enabling peer-to-peer transactions through blockchain technology. It enhances transparency, security, and accessibility, allowing users to access financial services such as lending, borrowing, and trading without relying on centralized institutions. Predictions for DeFi indicate exponential growth, with the integration of AI and machine learning driving significant advancements in fraud detection, risk assessment, and transaction analysis. Before the integration of AI, financial fraud detection primarily relied on rule-based systems, manual audits, and traditional statistical models—methods that lacked adaptability and real-time decision-making capabilities. Legacy systems such as credit scoring models and transaction monitoring frameworks struggled with scalability and required continuous human intervention. As fraudulent activities and cyber threats have grown more sophisticated, the limitations of traditional solutions have become increasingly apparent, underscoring the need for AI-driven approaches. By leveraging machine learning, transaction patterns can be analyzed with greater accuracy, enabling real-time anomaly detection and significantly reducing financial risk. The motivation behind this development is to enhance security, improve accuracy in transaction classification, and deliver scalable financial crime detection solutions. Conventional fraud detection mechanisms often fail to keep pace with evolving threats, resulting in considerable financial losses. Manual reviews are time-consuming and error-prone, while static models lack adaptability to emerging fraudulent behaviors. Machine learning enables real-time monitoring and predictive analysis, allowing financial institutions to detect suspicious activities with heightened precision. The proposed system integrates a combination of classifiers—including decision trees, logistic regression, AdaBoost, gradient boosting, k-nearest neighbors, and random forest algorithms—to improve transaction classification accuracy. AI-driven analysis enhances fraud detection by learning from historical data, reducing false positives, and enabling automated de-anonymization of transactions. This system applies advanced algorithms to identify fraudulent patterns, optimize financial security, and streamline transaction verification. By automating the process, AI-powered models offer a more robust and efficient approach to securing financial transactions, ensuring greater reliability and trust in decentralized finance.
Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.
This research aims to examine the regulatory compliance aspects of smart contracts within the Indonesian legal system using a Systematic Literature Review (SLR) approach. The review focuses on how smart contracts are recognized and regulated within Indonesia’s legal framework, the challenges related to consumer protection, their compatibility with traditional contract principles, and comparisons with international regulatory standards. From a total of 158 relevant studies, 50 articles were selected based on multi-layered search strategies, citation chaining, and relevance scoring. The findings reveal that while Indonesia has established a normative legal basis for recognizing electronic contracts, significant gaps persist in enforcement, legal clarity, and consumer protection. The implications of this review highlight the need for regulatory reform, the standardization of legal frameworks, and the integration of interdisciplinary approaches to secure the application of smart contracts within Indonesia’s growing digital economy.
The rapid rise of the prices of cryptocurrencies has intensified the need for robust forecasting models that can capture the irregular and volatile patterns. This study aims to forecast Bitcoin prices over a 15-day horizon by evaluating and comparing two distant predictive modeling approaches: the Bayesian State-Space model and Long Short-Term Memory (LSTM) neural networks. Historical price data from January 2024 to April 2025 is used for model training and testing. The Bayesian model provided probabilistic insights by achieving a Mean Squared Error (MSE) of 0.0000 and a Mean Absolute Error (MAE) of 0.0026 for training data. For testing data, it provided 0.0013 for MSE and 0.0307 for MAE. On the other hand, the LSTM model provided temporal dependencies and performed strongly by achieving 0.0004 for MSE, 0.0160 for MAE, 0.0212 for RMSE, 0.9924 for R2 in terms of training data and for testing data, and 0.0007 for MSE with an R2 of 0.3505. From the result, it indicates that while the LSTM model excels in training performance, the Bayesian model provides better interpretability with lower error margins in testing by highlighting the trade-offs between model accuracy and probabilistic forecasting in the cryptocurrency markets.
Tulika Shrivastava, Basem Suleiman, Sachit A. J. Desa, Muhammad Johan Alibasa · 6 authors
Abstract The volatility of cryptocurrencies necessitates reliable short-term price prediction models for informed investment decisions. This work presents two benchmarking studies that predict cryptocurrency price over hourly and daily time horizons using market indicators and social media data. Study 1 used BERT-based sentiment analysis of hourly Twitter data combined with financial indicators, while Study 2 applied VADER sentiment analysis to daily Twitter and Google Trends data alongside financial indicators. Both studies systematically evaluated statistical models (ARIMA, ARIMAX), machine learning approaches (SVR), and deep learning architectures (1D-CNN, LSTM) including ensemble, multi-modal, and hybrid configurations. Particular attention was given to the influence of lag periods, data aggregation, and sentiment analysis nuances on cryptocurrency price. Empirical results identify LSTM as the best-performing singular prediction model, achieving a 64.5% reduction in RMSE (4.56e $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 03) compared with the SVR baseline in Study 1. In Study 2, the hybrid LSTM + ARIMA model delivered the strongest performance, reducing RMSE by 32.5% (RMSE=2.55e+02) relative to the best performing singular baseline. Hybrid architectures combining LSTM with ARIMA or ARIMAX consistently achieved the lowest RMSE values, outperforming all other configurations and proving especially effective at capturing price movements and turning points. These findings demonstrate how combining statistical methods with deep learning can address non-stationarity, improve sentiment preprocessing, and enhance model interpretability.
Saad AL Azzam, Raenu Kolandaisamy, Ghassan AL Dharhani
Smart contracts (SCs) have become an essential component in the world of decentralized applications, automating transactions across blockchain networks without the need for intermediaries, and with this rise in adoption, the technology has also brought forth growing concern due to security vulnerabilities, which have led to serious financial damage, and the problem is far from being solved. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. Given these challenges, researchers have been actively exploring more advanced detection techniques. Despite progress, many existing studies tend to focus narrowly on specific methods, whether static analysis, dynamic testing, or machine learning models, without offering a comprehensive comparison across all available approaches. This fragmented landscape leaves a noticeable gap for practitioners looking for a well-rounded understanding of smart contract security solutions. To address this, our study set out to systematically review the existing body of work, analysing 21 reviewed studies published between 2020 and 2024. The primary aim was to combine the diverse techniques that have been proposed for detecting vulnerabilities in smart contracts, ranging from static and dynamic analyses to more recent AI-driven models, graph-based techniques, and hybrid systems, critically evaluating their strengths, weaknesses, and practical effectiveness. The methodology followed a structured approach. We searched major research databases, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and Scopus—using carefully crafted search queries to ensure that we captured the most relevant and up-to-date papers. Our findings revealed that AI-based methods, especially those leveraging deep neural networks and graph neural networks, have achieved impressive detection accuracy in controlled environments. For example, models such as ContractWard and SCVDIE-ENSEMBLE reported Micro-F1 scores of 98.48% and 95.46%, respectively, but these models also have a trade-off—they demand high computational resources, which limits their real-world deployment in resource-constrained settings. On the other hand, lighter tools such as Slither and NeuCheck offer faster detection and lower resource usage but might fall short in regard to identifying more complex or new vulnerabilities. We also noticed a growing trend towards real-time monitoring tools, such as SODA and GPTScan, which aim to strike a balance by reducing false positives while providing proactive security measures. However, several challenges remain unresolved where many AI-driven models still rely heavily on labelled datasets, which may not generalize well to novel attack patterns. Scalability is another concern, especially for models that are computationally intensive.
Ethereum, sebagai salah satu aset kripto utama, memiliki volatilitas harga yang tinggi, sehingga menciptakan kebutuhan akan model prediksi yang akurat untuk membantu pengambilan keputusan investasi. Penelitian ini bertujuan untuk mengimplementasikan kinerja dua model machine learning populer, yaitu Gated Recurrent Unit (GRU) yang merupakan model deep learning untuk data sekuensial, dan Extreme Gradient Boosting (XGBoost) yang merupakan model ensemble. Data yang digunakan adalah data historis harga harian Ethereum yang mencakup fitur Open, High, Low, Close, Volume (OHLCV). Metode penelitian meliputi tahap pra-pemrosesan data seperti normalisasi Min-Max Scaler dan pembagian data dengan rasio 80% data latih dan 20% data uji. Evaluasi kinerja kedua model diukur menggunakan metrik Root Mean Squared Error (RMSE) dan R-squared (R²). Hasil pengujian menunjukkan bahwa model GRU menghasilkan prediksi yang lebih baik, mencapai nilai RMSE 101.37 dan R² 0.9718, sedangkan model XGBoost memperoleh nilai RMSE 107.29 dan R² 0.9656. Hal ini mengindikasikan bahwa kemampuan GRU dalam menangkap pola dan dependensi temporal pada data deret waktu lebih unggul untuk kasus prediksi harga Ethereum. Kesimpulan dari penelitian ini adalah model GRU lebih efektif dan dapat diandalkan untuk memprediksi harga Ethereum dibandingkan XGBoost dalam penelitian ini.
This study examines how the historical development of web technologies and Korea’s digital transformation have shaped the structure of art consumption, situating the inquiry within broader socio-historical flows and institutional frameworks. The trajectory from the static information delivery of Web 1.0, to the interactive platforms of Web 2.0, to the semantic and relational information sharing of Web 3.0, and finally to the decentralization of Web3, extends beyond a mere technological evolution. In particular, the state-led informatization policies initiated in the wake of the 1997 IMF financial crisis, along with subsequent strategies such as the Digital New Deal, laid the institutional foundation for the digital transformation of the Korean art market. Within this context, the study traces the processes and changes in art consumption that emerged. Methodologically, the research adopts a qualitative, interpretive approach, drawing upon diverse secondary sources including academic studies, policy documents, news reports, and platform operation records. The findings demonstrate that web technologies have served as a primary driver of comprehensive changes in art—shaping modes of appreciation, systems of distribution, and extending further into assetization and financialization. Today, art consumption has been redefined to move beyond viewing and purchasing, encompassing decentralized experiences and transactions as assets via online platforms. This shift underscores the rise of platform-based relational consumption models and the structural incorporation of art into capital markets. The study’s significance lies in its diachronic analysis of how web technologies have impacted art consumption. Furthermore, by integrating perspectives from the histories of technology, policy, and art, it establishes a foundation for multi-layered inquiry into the transformations of art consumption and institutional frameworks.
Hafsteinn Hjartarson, Fjölnir Thrastarson, Anna Sigríður Íslind, Gísli Hjálmtýsson
Abstract Permissioned blockchains have gained prominence as a means of decentralizing trust while retaining controlled access, particularly in enterprise settings and regulated peer-to-peer environments. These systems offer advantages in scalability, performance, and security; however, challenges persist in effectively managing membership and its interaction with consensus protocols. Ethereum’s transition to Proof-of-Stake has also been a transition to managed membership, where validators are actively monitored and penalized for non-performance. This paper examines the dynamic tension between membership management and consensus protocols in permissioned blockchains, as well as the benefits of active management in improving overall system performance. In this paper, we propose a framework for dynamic membership management that includes actively admitting, monitoring, and ejecting members. Our approach decouples membership management from the underlying blockchain construction process. Our simulations confirm the potential benefits of managed membership, in part to facilitate lightweight mechanisms for improved performance and reliability. Our findings suggest that dynamic membership management is a critical area of study with significant implications for the future design of permissioned blockchains. Our contributions provide a conceptual foundation for designing dynamic membership protocols in permissioned blockchains, filling a gap in the literature and offering practical solutions to enhance blockchain performance in controlled environments.
This paper provides an in-depth review of the evolving role of quantum computing in the financial sector, emphasizing both its computational potential and cybersecurity implications. Distinguishing itself from existing surveys, this work integrates classical quantum computing applications - such as portfolio optimization, risk analysis, derivative pricing, and Monte Carlo simulations with a thorough examination of blockchain technologies and post-quantum cryptography (PQC), which are crucial for maintaining secure financial operations in the emerging quantum era. We propose a structured four-step framework to assess the feasibility and expected benefits of implementing quantum solutions in finance, considering factors such as computational scalability, error tolerance, data complexity, and practical implementability. This framework is applied to a series of representative financial scenarios to identify domains where quantum approaches can surpass classical techniques. Furthermore, the paper explores the vulnerabilities quantum computing introduces to digital finance-related applications and blockchain security, including risks to digital signatures, hash functions, and randomness generation, and discusses mitigation strategies through PQC and quantum-resilient alternatives of classical digital finance tools and blockchain architectures. By addressing both quantum blockchain, quantum key distribution (QKD) as well as quantum communication networks, his review presents a more holistic perspective than prior studies, offering actionable insights for researchers, financial practitioners, and policymakers navigating the intersection of quantum computing, blockchain, and secure financial systems.
The increasing adoption of smart home devices and IoT-based security systems presents significant opportunities to enhance convenience, safety, and risk management for homeowners and service providers. However, secure onboarding-provisioning credentials and establishing trust with cloud platforms-remains a considerable challenge. Traditional onboarding methods often rely on centralized Public Key Infrastructure (PKI) models and manufacturer-controlled keys, which introduce security risks and limit the user's digital sovereignty. These limitations hinder the widespread deployment of scalable IoT solutions. This paper presents a novel onboarding framework that builds upon existing network-layer onboarding techniques and extends them to the application layer to address these challenges. By integrating consortium blockchain technology, we propose a decentralized onboarding mechanism that enhances transparency, security, and monitoring for smart home architectures. The architecture supports device registration, key revocation, access control management, and risk detection through event-driven alerts across dedicated blockchain channels and smart contracts. To evaluate the framework, we formally model the protocol using the Tamarin Prover under the Dolev-Yao adversary model. The analysis focuses on authentication, token integrity, key confidentiality, and resilience over public channels. A prototype implementation demonstrates the system's viability in smart home settings, with verification completing in 0.34 seconds, highlighting its scalability and suitability for constrained devices and diverse stakeholders. Additionally, performance evaluation shows that the blockchain-based approach effectively handles varying workloads, maintains high throughput and low latency, and supports near real-time IoT data processing.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
The Decentralized Physical Infrastructure (DePIN) market is revolutionizing the sharing economy through token-based economics and smart contracts that govern decentralized operations. By 2024, DePIN projects have exceeded \$10 billion in market capitalization, underscoring their rapid growth. However, the unregulated nature of these markets, coupled with the autonomous deployment of AI agents in smart contracts, introduces risks such as inefficiencies and potential misalignment with human values. To address these concerns, we introduce EconAgentic, a Large Language Model (LLM)-powered framework designed to mitigate these challenges. Our research focuses on three key areas: 1) modeling the dynamic evolution of DePIN markets, 2) evaluating stakeholders' actions and their economic impacts, and 3) analyzing macroeconomic indicators to align market outcomes with societal goals. Through EconAgentic, we simulate how AI agents respond to token incentives, invest in infrastructure, and adapt to market conditions, comparing AI-driven decisions with human heuristic benchmarks. Our results show that EconAgentic provides valuable insights into the efficiency, inclusion, and stability of DePIN markets, contributing to both academic understanding and practical improvements in the design and governance of decentralized, tokenized economies.
Blockchain sharding is a promising solution for scalability but struggles to reach the expected performance due to the high ratio of cross-shard transactions. Account migration has emerged as a critical approach to optimizing shard performance. However, existing migration solutions suffer from inefficient handling of queued withdrawal transactions from a migrating account and inadequate priority mechanism for migration transaction, resulting in prolonged transaction makespan and reduced system throughput. This paper proposes Caravan, a novel blockchain sharding system for optimizing account migration. First, Caravan proposes a transaction aggregation-based migration scheme to efficiently handle withdrawal congestion post-migration. It incorporates a multi-level Merkle tree and cross-shard synchronization protocol to ensure cross-shard security. Second, Caravan presents an economic incentive-driven priority mechanism that motivates miners to perform transaction aggregation and prioritize migration transactions by increasing the associated revenue. Furthermore, its gas recycling strategy enables users to finance migration costs without awareness or extra expenses. Finally, we develop the Caravan prototype, deploy it on Alibaba Cloud, and experiment with real Ethereum transactions. The results show that compared to the state-of-the-art account migration schemes, Caravan significantly mitigates the transaction surge caused by migration, achieving up to a 3.2× throughput improvement and a 65% reduction in transaction confirmation latency. And users share considerable migration costs without extra expenses, significantly reduce system costs. The code for Caravan is available on GitHub.11Caravan are available athttps://github.com/Caravan-project/Caravan.
Financial transactions in Islam must adhere to sharia principles, avoiding the elements of gharar (uncertainty), maysir (speculation), and riba (interest). One of the most prominent technological innovations in this domain is the implementation of Smart Contracts in cryptocurrency trading on the Ethereum platform. However, their application still faces significant sharia compliance challenges, particularly concerning contractual uncertainty and the potential for misuse. This study aims to analyze the implementation mechanism of Smart Contracts from a sharia compliance perspective while exploring the opportunities and challenges of their adoption within the Ethereum Ponorogo Community. Employing a qualitative approach with a case study method, data was collected through interviews, observation, and documentation. The findings reveal that while Smart Contracts offer greater transparency, efficiency, and automation in transactions, challenges such as regulatory ambiguity, limited sharia literacy, and the inherent volatility of crypto assets remain major obstacles. Although the Ethereum Ponorogo Community has made efforts to avoid non-compliant elements, their practices are more "aspirational" and not yet fully guaranteed by a comprehensive sharia regulatory framework. This research concludes that a clear roadmap is needed, which includes a focus on education, stronger regulations (including fatwas from sharia authorities), and audits of Smart Contract code. By following this path, the technology can become a more ethical and sharia-compliant solution for crypto transactions, bridging the gap between technological innovation and the principles of Islamic economics.
Arshad Khan, Butch Dela Cruz, Narayan Nepal, Faheem Khan · 5 authors
This paper presentse-PrescripChain, a novel blockchain-based electronic prescription system enhanced with federated learning (FL) to address critical security, privacy, and efficiency challenges in traditional e-prescription platforms. While existing systems like New Zealand’s NZePS have improved prescription accuracy, centralized architectures remain vulnerable to fraud and data breaches, as evidenced by real-world incidents.e-PrescripChainleverages Ethereum smart contracts for tamper-proof prescription management, IPFS for decentralised storage of sensitive data, and FL for privacy-preserving collaborative fraud detection across healthcare institutions. Experimental results demonstrate the system’s practicality: AES-256 encryption handles 500 KB prescriptions in under 500 ms, while IPFS ensures reliable data retrieval (80–100 ms access times). By combining blockchain’s immutability with FL’s distributed intelligence, the framework achieves compliance with healthcare regulations (e.g., NZ Privacy Act 2020), mitigates single points of failure, and enables real-time prescription tracking. This work advances secure e-prescription systems by addressing the limitations of centralised models through a scalable, patient-centric approach.
This study conducts a scientometric analysis of global financial risk management research to map its intellectual structure, thematic trends, and collaboration networks over the period 2000–2025. Data were retrieved from the Scopus database using a comprehensive search strategy and analyzed with VOSviewer to visualize co-authorship patterns, country collaborations, keyword co-occurrences, thematic clusters, and temporal developments. The results indicate that risk management, risk assessment, and financial markets remain the most influential and frequently studied topics, while emerging themes such as sustainability, decentralized finance, cryptocurrency, and supply chain resilience reflect the field’s adaptation to evolving technological, economic, and environmental challenges. Collaboration analysis highlights the dominance of countries such as China, the United Kingdom, and India, alongside increasing participation from emerging economies. The study offers practical implications for policymakers and financial practitioners to align strategies with current research priorities, and theoretical contributions by identifying conceptual linkages and emerging research fronts. Limitations include reliance on a single database and the inherent biases of citation-based analysis.
Godfrey Murairidzi Gotora, Eva Tsitsi Chigodo, Godfrey Benjamin Zulu, Mfula Eunice
Since the synthesis and evolution of the coding and blockchain technology with the self-executing commands, there is a sudden shift to the smart contract consumption patterns. In the global virtual commerce this phenomenon has been enormously increasingly day by day. This has been so based on the distinct, clear and strong advantageous characteristics mainly lies in security, transparency and its unique way of its decentralized automation nature. A large scope of transactions of this technology’s usage has been implemented in virtual and argumentative reality where codes create a lot of services such as games and commercial services amongst end users basically with no lawyers involved. However, despite its wide adoption intensifies, it renders no immune from the potential risks and uncertainty issues like any other software-based platforms. In generic terms every industry needs a regulatory way, which oversee or set red lines of boundaries in the form of structures, organizations and policies. In this context the code written and protocols which are executed automatically in systems aught also to be vetted in legal judiciary systems.
ABSTRACT This paper proposes a solution for the issue of silent shareholders lacking influence over company decisions and not receiving adequate compensation. Thus, we adopt Palmon, Kleinman, and Medinets’s (2022) “capital contract” framework and extend it by integrating smart contract functionality. This study then introduces a prototype to demonstrate how this enhanced framework can be implemented through blockchain-based smart contracts. By linking silent shareholders’ dividends to executive compensation, these smart contracts enhance the trustworthiness and transparency of the compensation processes for executives and shareholders. What is more, blockchain-based smart contracts automate the contract terms, potentially reducing the need for intermediaries to monitor managerial actions. Also, smart contracts are flexible to meet diverse reporting requirements and adapt to the unique characteristics of a particular company. Data Availability: All data used in this study are available in the manuscript. JEL Classifications: M40; O33.
R. Vettriselvan, Palanivel Rathinasabapathi Velmurugan, P. Shalini, T. C. Catherin · 6 authors
As financial systems grow increasingly digitized, money laundering methods have become more complex and harder to detect. This chapter explores the dual role of AI in the digital economy,both as a tool for combating illicit financial activity and as a potential risk. With the rise of cryptocurrencies, decentralized finance, and automated financial platforms, traditional compliance mechanisms are struggling to keep pace. AI technologies offer powerful tools for improving transaction monitoring, identifying suspicious behavior, and enhancing real-time risk analysis. However, these technologies also raise concerns about bias, opacity, and regulatory accountability. Drawing on theoretical frameworks such as Routine Activity Theory and Strain Theory, the chapter examines structural vulnerabilities in digital finance. It also analyzes the current regulatory gaps that hinder effective enforcement. The study concludes with recommendations for ethically integrating AI into AML systems, strengthening global coordination, and updating compliance models for the digital era
Aiming at the problems of insufficient integrity assurance, high risk of privacy leakage, and low cross-departmental circulation efficiency in the field of government data security, this study proposes an optimized blockchain solution integrating an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and dynamic Attribute-Based Encryption (ABE). By constructing a government data security storage model, the data layer's hash verification mechanism (SHA-256 + Merkle Patricia Tree) and network layer's P2P protocol (with packet loss retransmission) are optimized based on typical government blockchain platforms (e.g., Nanjing Electronic License Sharing Platform). Experiments with 200 nodes simulated government networks, comparing TPS, data verification delay, and privacy protection before and after optimization. The results show that after optimization: TPS increased by 37.2% (from 89.6 to 123.0), cross-departmental data verification time was shortened to 0.42 seconds, and zero-knowledge proof was used to realize anonymized query of sensitive fields (such as ID card numbers and real estate information). The research indicates that this technical solution can effectively resolve the contradictions among integrity, privacy, and circulation of government data, providing technical references for the engineering implementation of government blockchain systems.