To alleviate difficulties in writing smart contracts for distributed blockchain applications, as other research, we propose transformation of Business Process Model and Notation (BPMN) models into blockchain smart contracts. Unlike other research, we use Discrete Event Hierarchical State Machine (DE-HSM) multi-modal modeling to identify collaborative trade transactions that need to be supported by the smart contract and describe how the trade transactions, that may be nested, are supported by a transaction mechanism. We describe algorithms to (i) identify the nested trade transactions and to (ii) transform the BPMN model into blockchains smart contracts that include a transaction mechanism to enforce the transactional properties for the identified trade transactions. The developed proof of concept shows that our approach to automated transformation of BPMN models into smart contracts with the support of privacy and cross-chain interoperability is feasible. The thesis examines and evaluates automatically generated alternative transaction mechanisms to support such transactions using three use cases of varying degree of complexity, namely order processing, supply chain management, and a multi-faceted trade use case. The research enriches the academic dialogue on blockchain technology and smart contracts and proposes potential avenues for future research.
Kareem Shehata, Han Fangqi, Sri AravindaKrishnan Thyagarajan
Traditionally, threshold secret sharing (TSS) schemes assume all parties have equal weight, yet emerging systems like blockchains reveal disparities in party trustworthiness, such as stake or reputation. Weighted Secret Sharing (WSS) addresses this by assigning varying weights to parties, ensuring security even if adversaries control parties with total weight at most a threshold $t$. Current WSS schemes assume honest dealers, resulting in security from only honest-but-curious behaviour but not protection from malicious adversaries for downstream applications. \emph{Verifiable} secret sharing (VSS) is a well-known technique to address this, but existing VSS schemes are either tailored to TSS, or require additional trust assumptions. We propose the first efficient verifiable WSS scheme that tolerates malicious dealers and is compatible with the latest CRT-based WSS~\cite{crypto_w_weights}. Our solution uses Bulletproofs for efficient verification and introduces new privacy-preserving techniques for proving relations between committed values, which may be of independent interest. Evaluation on Ethereum show up to a $100\times$ improvement in communication complexity compared to the current design and $20\times$ improvement compared to unweighted VSS schemes.
Gordon Y. Liao, Ziming Zeng, Mira Belenkiy, Jacob Hirshman
This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose a system that analyzes transaction patterns to identify wallets with close connections to centralized exchanges. Our directed graph analysis of the Ethereum blockchain reveals that 56% of large USDC wallets (with a lifetime maximum balance greater than \$10,000) are EAI and 88% are within one transaction hop of an EAI. For transactions exceeding \$2,000, 91% involve at least one EAI. Crucially, an analysis of past exploits shows that 83% of the known exploiter addresses are not EAIs, with 21% being more than five hops away from any regulated exchange. We present three implementation approaches with varying gas cost and privacy tradeoffs, demonstrating that EAI-based access control can potentially prevent most of these incidents while preserving blockchain openness. Importantly, our approach does not restrict access or share personally identifiable information, but it provides information for protocols to implement their own validation or risk scoring systems based on specific needs. This middle-ground solution enables programmatic compliance while maintaining the core values of open blockchain.
Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev · 5 authors
This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases.
Bassam W. Aboshosha, M.A. Zayed, Hany S. Khalifa, Rabie Α. Ramadan
Abstract Background The rapid expansion of Internet of Things applications in healthcare has created new opportunities for improving patient care through real-time monitoring and data sharing. However, this growth also introduces significant challenges related to data security, privacy, and system efficiency, especially for devices with limited processing power and energy resources. To address these issues, this study introduces a blockchain-based lightweight hashing system specifically designed for healthcare environments with resource-constrained devices. The goal is to ensure secure, efficient, and scalable handling of sensitive medical data without overwhelming the capabilities of connected devices. Results The proposed system combines a collision-resistant, lightweight hash function with blockchain technology to enhance data integrity, authentication, and privacy. The hash function minimizes computational demands, making it ideal for wearable and embedded healthcare devices. Blockchain integration enables decentralized data management, preventing unauthorized access and tampering. The system generates unique, immutable patient identifiers and protects electronic health information from common security threats, including collision attacks, Sybil attacks, and cryptographic analysis. Simulation results show improved computational efficiency, lower latency, and effective handling of high transaction volumes with minimal resource usage. Conclusions This research presents a secure and efficient framework for managing medical data in healthcare Internet of Things applications. By leveraging lightweight cryptographic techniques and decentralized data structures, the system addresses key limitations in current solutions while supporting scalability and real-world deployment. Potential applications include secure patient monitoring, real-time sharing of health data, and decentralized management of medical records. The proposed approach provides a foundation for future advancements in digital healthcare systems, particularly in remote care, emergency response, and wearable health technologies.
A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies-governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules. Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions-credit, insurance, token exchange, and investment-as expressions of the same underlying principle under varying conditions. By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation-offering a new foundation for modeling decentralized financial behavior in both human and artificial agents.
This study builds a decentralized authentication system for art institutions to solve the information island problem and combat counterfeiting in traditional art tracing. Through the fusion architecture of blockchain and the InterPlanetary File System (IPFS), distributed storage and cross-chain verification of work metadata are realized. Smart contracts automatically execute ownership registration, cross-border transfer, and other processes, and IPFS nodes ensure immutable storage of high-definition images and identification reports. A prototype system based on React.js and Node.js, integrating Ethereum smart contracts and the MetaMask wallet module. In a simulated commercial environment, a pressure test was conducted on 50 paintings and digital calligraphy collections. The system completed transaction confirmation in an average of 3 seconds, and the mass transaction reduced authentication costs by 62%. Specifically tailored to the practical needs of emerging art institutions in China, the program supports a hybrid collection management model for physical art and NFTs, and provides a reliable technical infrastructure for cross-border art financing.
The accelerating digitization of healthcare has amplified the demand for secure, interoperable, and privacy-preserving information systems capable of managing sensitive patient data across diverse institutions. Traditional Health Information Systems (HIS) often struggle with fragmentation, data breaches, and lack of trust, posing significant barriers to integrated care and real-time medical decision-making. Blockchain technology—characterized by its decentralized architecture, cryptographic security, and immutability—offers a transformative paradigm for healthcare data management. This paper explores the development and deployment of Blockchain-Powered Health Innovation Information Systems (BHIIS), focusing on their potential to enable secure, verifiable, and scalable exchange of electronic health records (EHRs) across providers, payers, and public health institutions. By combining distributed ledger technology with smart contracts, BHIIS can automate data-sharing permissions, enhance patient control over personal health data, and ensure traceable access logs that comply with regulatory standards such as HIPAA and GDPR. This study examines architectural frameworks that integrate blockchain with interoperable health data standards (e.g., HL7 FHIR), enabling seamless communication among heterogeneous systems without compromising privacy. We evaluate consensus mechanisms, off-chain storage strategies, and identity management schemes that address scalability and data ownership concerns in real-world healthcare networks. Furthermore, the paper analyzes emerging use cases—including pandemic response, clinical trials, and chronic disease management—where blockchain-enhanced systems have demonstrated tangible benefits in accuracy, transparency, and trust. Ethical and infrastructural considerations, such as stakeholder governance, energy consumption, and digital divide challenges, are also discussed. By presenting a roadmap for implementing BHIIS, this work contributes to shaping next-generation health IT ecosystems that prioritize patient-centricity, resilience, and innovation.
In an increasingly digital world, establishing secure and reliable methods for verifying identity has become a critical priority across sectors such as finance, healthcare, education, and e-governance. Traditional authentication mechanisms—relying on passwords, personal identification numbers, and physical documents—are increasingly susceptible to fraud, data breaches, and user inconvenience. This paper presents a multi-modal biometric framework for digital identity management, integrating facial recognition and fingerprint verification to enhance accuracy, reduce fraud, and ensure user-centric security. The proposed system includes modules for data acquisition, preprocessing, feature extraction using Convolutional Neural Networks (CNNs) and minutiae detection, score-level fusion, and final authentication decisions. Security and privacy are ensured through AES-256 encryption, differential privacy techniques, and decentralized blockchain-based data storage. This research contributes a scalable, privacy-aware, and highly accurate digital identity model capable of addressing challenges such as interoperability, user trust, and regulatory compliance. Future enhancements include the integration of additional biometric modalities and deployment in mobile and IoT environments.
The rapid growth of artificial intelligence (AI) in decentralized systems such as healthcare, financial networks, and autonomous transportation has underscored the critical need for interpretability, fairness, and verifiable trust in decision-making. Traditional federated learning frameworks, while addressing data privacy and scalability, often suffer from bias propagation, opaque model behaviors, and limited mechanisms for ensuring accountability. This article introduces Chain-of-Trust AI, a novel paradigm that integrates zero-knowledge proofs (ZKPs), federated reinforcement learning (FRL), and generative learning models to create an interpretable, bias-free, and verifiable decision-making framework for complex distributed environments. The proposed framework leverages FRL to enable adaptive coordination across heterogeneous agents while maintaining local data sovereignty. Generative learning models, such as variational autoencoders, provide transparent causal representations that support bias detection and enhance interpretability of reinforcement-driven policies. ZKPs are embedded as cryptographic guarantees to verify model updates and decision outcomes without exposing sensitive information, thus ensuring compliance, trust, and transparency across decentralized networks. Methodologically, the framework is evaluated through MATLAB-based multi-agent simulations, benchmarking performance in terms of interpretability, fairness indices, convergence stability, and verification overhead. Theoretical analyses confirm convergence under heterogeneous reward structures, cryptographic soundness of proofs, and bias reduction capabilities through generative regularization. Case studies in decentralized healthcare diagnostics, financial fraud detection, and autonomous vehicular coordination highlight the practical scalability and robustness of Chain-of-Trust AI. By uniting reinforcement learning, generative interpretability, and zero-knowledge verification, this work pioneers a secure, auditable, and ethically aligned AI architecture for decentralized complex systems, advancing both technical rigor and governance in distributed intelligence.
Ahmad Zeb, Surayya Jamal, N. Irfan, Hafiz M. Sohail · 5 authors
Objective:This study investigates whether individual preferences for Environmental, Social, and Governance (ESG) principles influence portfolio decisions related to crypto assets. While ESG-focused investing is widely observed in traditional finance, less is known about how these preferences affect exposure to controversial assets such as cryptocurrencies, particularly considering their environmental concerns (e.g., energy-intensive mining processes). Methodology:The analysis uses data from the 2023 Austrian Household Finance and Consumption Survey (HFCS), which includes responses from a nationally representative sample of 2,000 individuals. The study employs descriptive statistics to profile investors, Pearson correlation analysis to examine bivariate relationships, and Ordinary Least Squares (OLS) regression to assess the effect of ESG preferences on crypto-asset investment, controlling for age, risk tolerance, and financial literacy. Results:The findings reveal a statistically significant and positive relationship between ESG preferences and crypto-asset exposure. ESG-conscious individuals are more likely to invest in cryptocurrencies than in traditional asset classes such as bonds or equities. The OLS model indicates that a one-unit increase in ESG preference score is associated with an average increase of €302 in crypto holdings, holding other variables constant. Correlation analysis supports this, with a coefficient of r = 0.28 between ESG scores and crypto exposure. Conclusion:Despite concerns over the environmental impact of certain cryptocurrencies, ESG-minded investors show notable engagement with crypto-assets—likely driven by innovation, decentralization values, or interest in ESG-aligned blockchain projects. These findings suggest a shift in how sustainable investing is understood in the digital age and underscore the need for nuanced ESG frameworks in crypto markets. The study offers valuable insights for policymakers, asset managers, and sustainability advocates aiming to guide the future of responsible digital finance.
Minh Tri Le, O. M. Harris, Charlotte Bennett, Fiona Greene
Amid the rapid development of Web3.0 technologies and blockchain infrastructures, the commercial real estate industry is experiencing a significant shift toward digitalization. This study proposes a tokenization framework for commercial real estate assets, grounded in the ERC-1400 standard. The system enables precise asset share registration via smart contracts and ensures regulatory compliance through on-chain KYC authentication and identity mapping mechanisms. To address liquidity challenges, an off-chain valuation oracle and a decentralized finance (DeFi) collateralization model are integrated into the architecture, enhancing the tradability of tokenized real estate assets. Simulation experiments and empirical analyses were conducted to evaluate title confirmation efficiency, asset liquidity, and operational controllability. The results demonstrate that, compared to conventional methods, the proposed system improved title confirmation efficiency by 99.6% (t = 327.4, p < 0.001), increased average daily transaction volume by 327% (χ² = 158.6, p < 0.001), achieved 100% transaction accuracy in 1,500 simulations, and successfully identified and intercepted 47 abnormal transactions via the KYC mechanism. These findings provide both a viable technical approach and theoretical basis for implementing real estate asset tokenization in practice, contributing to the secure and scalable integration of traditional assets into decentralized ecosystems.
This article examines how artificial intelligence and decentralized technologies can transform healthcare supply chains to enhance national biosecurity. A comprehensive framework integrating predictive analytics, autonomous logistics, and distributed manufacturing is presented to create resilient healthcare ecosystems capable of withstanding pandemics, geopolitical conflicts, and cyber threats. Long Short-Term Memory networks and reinforcement learning algorithms offer unprecedented capabilities for demand forecasting and resource allocation, while Graph Neural Networks optimize medical distribution routes with improved efficiency. Blockchain technology provides tamper-proof transparency throughout pharmaceutical supply chains, and additive manufacturing enables localized production of critical supplies during disruptions. Digital twin simulations allow healthcare organizations to anticipate potential shortages before they materialize. Implementation challenges include data interoperability barriers, infrastructure limitations in developing regions, algorithmic bias risks, and data privacy concerns, all of which can be addressed through standardized exchange formats, coordinated investment strategies, formal fairness assessments, and federated learning approaches.
Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to embed natural language messages into transactions. Users can leverage these Input Data Messages (IDMs) for peer-to-peer communication. This means that, beyond Ethereum's well-known role as a financial infrastructure, it also serves as a decentralized communication medium. We present the first large-scale analysis of Ethereum IDMs from the genesis block to February 2024 (3134 days). We filter IDMs to extract 867,140 transactions with informative IDMs and use LLMs for language detection. We find that English (95.4%) and Chinese (4.4%) dominate the use of natural languages in IDMs. Interestingly, English IDMs center on security and scam warnings (24%) with predominantly negative emotions, while Chinese IDMs emphasize emotional expression and social connection (44%) with a more positive tone. We also observe that longer English IDMs often transfer high ETH values for protocol-level purposes, while longer Chinese IDMs tend to involve symbolic transfer amounts for emotional intent. Moreover, we find that the IDM participants tend to form small, loosely connected communities (59.99%). Our findings highlight culturally and functionally divergent use cases of the IDM channel across user communities. We further examine the security relevance of IDMs in on-chain attacks. Many victims use them to appeal to attackers for fund recovery. IDMs containing negotiations or reward offers are linked to higher reply rates. We also analyze IDMs' regulatory implications. Their misuse for abuse, threats, and sexual solicitation reveals the urgent need for content moderation and regulation in decentralized systems.
The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead on edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy-preserving mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments-particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount. To address these challenges, this paper introduces the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework, which integrates blockchain with hybrid privacy mechanisms and intelligent aggregation strategies. The architecture comprises three layers: (1) an Edge Nodes Layer for on-device learning; (2) a Federated Aggregation Layer using Secure Multi-Party Computation (SMPC) and Differential Privacy (DP); and (3) a Blockchain Layer with a lightweight PoS + BFT consensus mechanism. Experimental evaluation on CIFAR-10 demonstrates 95.2% accuracy, a 43% reduction in communication latency, a 37% decrease in computational cost, and robust defense against data/model poisoning and adversarial attacks. Attack resilience improved accuracy from 72.5 to 93.2%, while privacy budget tuning achieved 90.3% accuracy at ε = 1.0. Compared to state-of-the-art models, EPP-BCFL exhibits superior performance in terms of security, scalability, and support for edge device heterogeneity, validating its applicability in secure EHR management.
This article explores the philosophical foundations of the North American trust and its connection to blockchain technology, proposing that Decentralized Autonomous Organizations (DAOs) represent its direct technological evolution. Rooted in Common Law traditions, the trust derives from an emphasis on negative liberty, where individuals shield their property from state intervention by transferring legal ownership to a trustee. This arrangement maintains autonomy and resists potential shifts in government regulation or policy. DAOs, driven by self-executing smart contracts, retain the trust’s fundamental purpose—protecting assets and ensuring independent governance—while eliminating reliance on a single fiduciary. By dispersing administrative power across a decentralized network, DAOs enhance resilience against political and economic unpredictability. However, European jurisdictions, influenced by Rousseau’s notion of positive liberty, traditionally subordinate property to the collective interest, thus restricting trusts and, more recently, imposing regulatory measures on blockchain organizations. In Europe, this idea often collides with legal frameworks that prioritize public interests, leading to regulatory scrutiny of blockchain-based systems. Despite these challenges, DAOs continue to refine the trust’s core principles in a technologically advanced environment, offering security and autonomy. Ultimately, this evolution reaffirms the enduring tension between individual freedom from governmental authority that underpins the Anglo-Saxon legal tradition.
In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviors, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.
Zaäfri A. Husodo, Md. Bokhtiar Hasan, Humaira Tahsin Rafia, Masagus M. Ridhwan · 6 authors
This study investigates the interconnected dynamics among diverse digital currencies, specifically focusing on risk-adjusted returns, tail risks, dynamic spillovers, and portfolio implications. Unlike prior research, which typically examines individual digital currency classes separately or in limited combinations, our study integrates six distinct classes of digital currencies, namely Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins, non-fungible tokens (NFTs), decentralized finance (DeFi) assets, and conventional cryptocurrencies, enabling direct comparisons of risk-return dynamics and systemic interdependencies. Using Value at Risk (VaR), Conditional Value at Risk (CVaR), quantile-based Vector Autoregression (Quantile VAR), and network connectedness analysis, we provide nuanced insights into the behavior of these assets across various market conditions (bullish, bearish, and normal states). Our results demonstrate that conventional cryptocurrencies and DeFi assets consistently deliver positive risk-adjusted returns, whereas Islamic gold-backed cryptocurrencies exhibit notably higher downside risks and negative performance. Spillover analysis reveals pronounced connectedness, particularly in extreme market states, with conventional cryptocurrencies identified as primary transmitters of market shocks and gold-backed stablecoins and Islamic gold-backed cryptocurrencies as recipients. Our findings underscore significant diversification opportunities offered by pairs of assets exhibiting low connectedness, especially in normal market conditions. Furthermore, portfolio optimization analysis highlights the superior hedging effectiveness and lower hedging costs associated with gold-backed stablecoins and conventional cryptocurrency pairs. This comprehensive investigation delivers critical implications for investors, suggesting informed strategies for asset allocation and risk management. Policymakers can also utilize our insights to design adaptive regulatory frameworks that address systemic risks arising from digital currency markets. ACKNOWLEDGMENT Gazi Salah Uddin gratefully acknowledges the Faculty of Economics and Business, Universitas Indonesia, for the academic appointment as Adjunct and Visiting Professor, and expresses sincere appreciation for the institutional support and research facilities extended during his residency, which significantly contributed to the completion of this work.
Distributed trust systems have been transformed by blockchain technology; however, scalability and privacy preservation remain major obstacles. Blockchain-based ridesharing platforms, which provide decentralization, privacy, and enhanced user control inside the system, have been offered as a solution to these problems. Blockchain-based ridesharing services have scaling problems in spite of these benefits. These systems' performance declines with an increase in users, which restricts their usefulness in high-volume marketplaces. To overcome these constraints, this study investigates how blockchain topologies can use zero-knowledge proof (ZKP) approaches. We provide a thorough examination of the current ZKP implementations in blockchain systems, such as zk-Rollups and Bulletproofs, assessing their theoretical underpinnings, real-world uses, and performance indicators. Our study shows that although ZKP integration can increase throughput through rollup technologies and greatly improve privacy guarantees, computational overhead and implementation difficulties continue to be obstacles to broad adoption. Provide a framework for ZKP integration that is tailored to balance privacy, scalability, and usability. This could move blockchain technology closer to more useful real-world applications.
This article examines the technological emergence trajectory of Art Non-Fungible Tokens (NFTs), exploring their initial promise and then failure as transformative commodities disrupting art economies. Operating within an analytical framework of hope, hustle and hype, death and taxes, we investigate the interplay of technological, cultural, and economic trends shaping this trajectory towards failure. We identify the sociotechnical imaginaries clothing art NFTs and consider their relationship to both the acceptance and rejection of this technology. Our analysis contends that the desire to escape economic exclusion created a collective hope through which social adoption occurred. However, delving into the digital graveyards of Art NFTs, we identify external forces such as cultural shifts, social backlash, and regulatory interventions extinguishing the public’s ‘cruel optimism’, leading to the revocation of the social licence to operate for this emerging technology.
The use of Electric Vehicles (EV) will promote urban sustainability, decrease air pollution, and reduce noise pollution. In this landscape a new mobility concept termed shared electric mobility-as-a-service (eMaaS) has emerged over the years. Shared eMaaS comprises the seamless integration of various forms of electric transport services available via one single digital platform. Although, the current shared eMaaS solutions are based mostly on fragmented and siloed systems which has resulted to issues related to the exchange of data and services from different eMaaS providers. Therefore, there is need for integrators and enablers to achieve an inter-operable and intra-operable seamless shared eMaaS. To this end, Distributed Ledger Technologies (DLT) is proposed in this study to enable new business models for shared electric mobility solutions. As compared to conventional approaches DLT offers a transparent, cost-efficient, and decentralized services both for managing the supply and demand sides of shared eMaaS to improve public transportation. Accordingly, this article presents a DLT based business models grounded on the literature to decentralize shared eMaaS. Qualitative data is collected from Scopus and Web of Science database, and descriptive analysis is employed to analyze the collected data. Findings from this study presents use case scenarios of how IOTA tangle as a DLT using smart contracts and IOTA wallet/tokens are deployed to design novel business models for managing seamless travel experience for electric car sharing and leasing to improve public transportation.
Juan Alamrio Berrios Moya, John Ayoade, Md. Ashraf Uddin
Academic credential fraud presents a significant challenge to the global academic and labor markets, undermining the credibility of legitimate qualifications. In this paper, we introduce ZKBAR-V, a Zero-Knowledge Proof-Enabled Blockchain-Based Academic Record Verification System. This system is designed to provide a privacy-preserving, immutable, and secure framework for managing academic credentials. The proposed system leverages zkEVM smart contracts on a blockchain-based infrastructure that enables credential verification without exposing underlying data. The approach integrates Decentralized Identifiers (DIDs) to standardize identity management while eliminating reliance on centralized authorities. We have used dual-blockchain, which separates public and private information, which can enhance both efficiency and privacy. In addition, this approach employs the Interplanetary File System (IPFS) for decentralized and secure document storage. ZKBAR-V is designed as an open-source, interoperable solution with a standardized Application Programming Interface (API) for seamless integration. We implemented the system and conducted comprehensive testing, which demonstrates its capability to manage transactions securely, maintain privacy, and reduce costs compared to traditional Ethereum mainnet-based solutions. By combining advanced blockchain technologies, decentralized storage, and globally unique identifiers, ZKBAR-V offers a scalable, adaptable, and robust solution for academic credential management. This strategy can significantly enhance credential integrity, promote global student mobility, and provide institutions worldwide with a trustworthy and efficient verification system.
The democratization of generative AI introduces new forms of human-AI interaction and raises urgent safety, ethical, and cybersecurity concerns. We develop a socio-technical explanation for how generative AI enables and scales cybercrime. Drawing on affordance theory and technological amplification, we argue that generative AI systems create new action possibilities for cybercriminals and magnify pre-existing malicious intent by lowering expertise barriers and increasing attack efficiency. To illustrate this framework, we conduct interrupted time series analyses of two large datasets: (1) 464,190,074 malicious IP address reports from AbuseIPDB, and (2) 281,115 cryptocurrency scam reports from Chainabuse. Using November 30, 2022, as a high-salience public-access shock, we estimate the counterfactual trajectory of reported cyber abuse absent the release, providing an early-warning impact assessment of a general-purpose AI technology. Across both datasets, we observe statistically significant post-intervention increases in reported malicious activity, including an immediate increase of over 1.12 million weekly malicious IP reports and about 722 weekly cryptocurrency scam reports, with sustained growth in the latter. We discuss implications for AI governance, platform-level regulation, and cyber resilience, emphasizing the need for multi-layer socio-technical strategies that help key stakeholders maximize AI's benefits while mitigating its growing cybercrime risks.
P. Chinnasamy, B. Subashini, Ramesh Kumar Ayyasamy, Ajmeera Kiran · 7 authors
The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.