The rapid rise of generative AI has made secure and scalable prompt engineering more important than ever. But there’s a catch — prompts often carry sensitive, proprietary, or regulated information, which brings real security and privacy risks. In response, our research presents a blockchain-powered framework designed for confidential prompt sharing. It brings together smart contracts, zero-knowledge proofs (ZKPs), and a hybrid consensus model that blends Proof-of-Stake with Byzantine Fault Tolerance (PoS-BFT). This combination doesn’t just enhance security — it also boosts energy efficiency, scalability, and resilience. When benchmarked against platforms like Ethereum and Hyperledger Fabric, our framework delivered lower latency, better performance, and strong reliability even under adversarial conditions. Plus, it’s built to work handin-hand with agentic AI workflows and decentralized identity systems, offering a flexible, futureready foundation for secure collaboration between AI agents.
The dominant paradigm of the modern internet, built on graphical user interfaces (GUIs) and discrete web applications, forces users into a fragmented and manual process of interaction, fundamentally limiting the complexity of tasks that can be automated. This paper argues for a new architectural paradigm: an AI-powered network of autonomous digital agents that replaces manual navigation with goal-oriented, natural language-based service procurement. To establish the necessity and viability of this approach, a critical literature review of dominant service-oriented architectures is conducted. The analysis reveals that Microservice Architecture (MSA), despite its advantages, suffers from inherent complexities in communication, discovery, and data management that undermine true service autonomy. A review of its predecessors finds that Service-Oriented Architecture (SOA) was hindered by centralized bottlenecks, while the Semantic Web’s vision of a machine-readable web failed due to the rigidity and complexity of its formal, logic-based approach. Furthermore, modern Web3 architectures, while offering decentralization, are shown to have severe limitations in scalability, cost, and flexibility that make them unsuitable for dynamic agent collaboration. The paper concludes that these existing paradigms contain fundamental gaps and posits that a Multi-Agent System (MAS) architecture provides a more robust and appropriate foundation for building a truly autonomous, post-GUI digital ecosystem.
The decentralized ecosystem is claimed to avoid security risks caused by centralization. Decentralized services, such as crypto wallets and decentralized applications (DApps), are purported to offer more reliable security and better protect user privacy. However, our research suggests a different reality: centralized components or scenarios are still prevalent within decentralized ecosystems, introducing security risks typically associated with centralization. This work systematically investigated the centralized security risks in crypto wallets and DApps. We found seven security risks and developed a series of methods to identify these risks. The detection results indicate that centralized security risks are widespread in the decentralized ecosystem. Among the 28 Ethereum-recommended crypto wallets, 96.4% have security risks. Of the 78 Web3 sites (frontends of DApps), 100% contain third-party scripts, and 44.9% expose the user's address to third parties. Furthermore, we developed a high-precision automated tool and inspected 110,506 on-chain smart contracts (backends of DApps), discovering that 83.5% contain at least one security risk. These risks affect 260 well-known tokens with a combined market capitalization exceeding${\$}$98 billion.
The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3–5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000–55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.
Saeed Moradi, Koosha Esmaeilzadeh Khorasani, Sara Rouhani
Interoperability is essential for transforming blockchains from isolated networks into collaborative ecosystems, unlocking their full potential. While significant progress has been made in public blockchain interoperability, bridging permissioned and permissionless blockchains poses unique challenges due to differences in access control, architectures, and security requirements. This paper introduces a blockchain-agnostic framework to enable interoperability between permissioned and permissionless networks. Leveraging cryptographic techniques, the framework ensures secure data exchanges. Its lightweight architectural design simplifies implementation and maintenance, while the integration of Clover and Dandelion++ protocols enhances transaction anonymity. Performance evaluations demonstrate the framework's effectiveness in achieving secure and efficient interoperability by measuring the forwarding time, the throughput, the availability, and their collusion impact of the system across heterogeneous blockchain ecosystems.
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs. Users lack recourse against service downgrades (model swaps, quantization, graph rewrites, or discrepancies like altered ad embeddings). Verifying outputs is hard because floating-point(FP) execution on heterogeneous accelerators is inherently nondeterministic. Existing approaches are either impractical for real FP neural networks or reintroduce vendor trust. We present TAO: a Tolerance Aware Optimistic verification protocol that accepts outputs within principled operator-level acceptance regions rather than requiring bitwise equality. TAO combines two error models: (i) sound per-operator IEEE-754 worst-case bounds and (ii) tight empirical percentile profiles calibrated across hardware. Discrepancies trigger a Merkle-anchored, threshold-guided dispute game that recursively partitions the computation graph until one operator remains, where adjudication reduces to a lightweight theoretical-bound check or a small honest-majority vote against empirical thresholds. Unchallenged results finalize after a challenge window, without requiring trusted hardware or deterministic kernels. We implement TAO as a PyTorch-compatible runtime and a contract layer currently deployed on Ethereum Holesky testnet. The runtime instruments graphs, computes per-operator bounds, and runs unmodified vendor kernels in FP32 with negligible overhead (0.3% on Qwen3-8B). Across CNNs, Transformers and diffusion models on A100, H100, RTX6000, RTX4090, empirical thresholds are $10^2-10^3$ times tighter than theoretical bounds, and bound-aware adversarial attacks achieve 0% success. Together, TAO reconciles scalability with verifiability for real-world heterogeneous ML compute.
Fernando Castillo, Eduardo Brito, Sebastian Werner, Pille Pullonen · 5 authors
Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic foundations for genuine trustworthiness in service ecosystems. Our approach starts with machine-readable SLA clauses converted into verifiable predicates and monitored within Trusted Execution Environments. These monitors collect timestamped telemetry, organize measurements into Merkle trees, and produce signed attestations. Zero-knowledge proofs aggregate Service-Level Indicators to evaluate compliance, generating cryptographic proofs verifiable by stakeholders, arbitrators, or insurers in disputes, without accessing underlying data. This ensures three security properties: integrity, authenticity, and validity. Our prototype demonstrates linear scaling up to over 1 million events per hour for measurements with near constant-time proof generation and verification for single violation claims, enabling trustless SLA enforcement through cryptographic guarantees for automated compliance verification in service monitoring.
Rapid advancement in generative AI and large language models (LLMs) has enabled the generation of highly realistic and contextually relevant digital content. LLMs such as ChatGPT with DALL-E integration and Stable Diffusion techniques can produce images that are often indistinguishable from those created by humans, which poses challenges for digital content authentication. Verifying the integrity and origin of digital data to ensure it remains unaltered and genuine is crucial to maintaining trust and legality in digital media. In this paper, we propose an embedding-based AI image detection framework that utilizes image embeddings and a vector similarity to distinguish AI-generated images from real (human-created) ones. Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content, while human-created images cluster similarly within their domain. To validate this hypothesis, we developed a system that processes a diverse dataset of AI and human-generated images through five benchmark embedding models. Extensive experimentation demonstrates the robustness of our approach, and our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions. Our solution provides a generalizable framework for AI-generated image detection that balances accuracy with computational efficiency.
This study examines the role of blockchain-based smart contracts' influence on financial transparency and effectiveness in the economic activities of the emerging markets. In this study, the researchers utilised a mixed-method approach that includes a systematic literature review, comparative case studies from Africa, Southeast Asia, and Latin America, and expert interviews. The research findings evidence that the adoption of smart contracts can lower transaction costs, eliminate intermediary services, improve trust in financial systems, and serve as alternatives to the current financial systems. The results further demonstrate that smart contracts can improve financial inclusion through low-cost microfinance, insurance, and trade finance solutions, as well as enhance trust and transparency with immutable records and real-time auditing. Nevertheless, weaknesses in infrastructure, digital literacy, and regulatory uncertainty create difficulties for adoption. In addition, the study augments the existing prior research emphasising the impacts of financial technology innovation in emerging markets by offering findings that are beneficial to the market stakeholders including policymakers, financial services institutions, and technology innovators, by effectively positioning blockchain-based solutions implementation as better and viable option that can drive inclusive financial development in the emerging economies.
D. Chandravathi, M. Swetha, S.K. Khaja Shareef, Apathi Haripriya · 6 authors
Mass adoption of online learning and remote assessment has created significant challenges to maintaining exam integrity, verifying examinees, and ensuring veracity in scores. Existing solutions are frequently not based on consistent and robust validation mechanisms, and it is easy to commit impersonation, cheating and manipulation of the results. This research proposes deployment of a blockchain-based trust architecture to improve security in online examination systems by using smart validation. The primary objective is to create an all-inclusive system that ensures the veracity of exam takers and the validity of exam records when integrating biometric, behavioral, and the use of smart contract automation. We created a dataset of 50 scenarios from exams that included real world elements (face match confidence, keystroke patters, gaze tracking difference, device information and time logs). Deep learning algorithms were used on the dataset for session classification while dynamic validation criteria were applied by smart contracts and a private Ethereum blockchain for validated results maintained. Four main methods were applied to review the proposed model: rule-based logic, random forest, logistic regression and support vector machine (SVM) models. The outcome showed that the proposed technique gave an accuracy of 92%, an F1-score of 0.92 and a ROC-AUC of 0.95 which is lower than the other approaches. By examining confusion matrices and performing statistical tests, it was proved that the suggested model is both robust and generalizable and it has an unbelievably low p-value of 1.95 × 10−20in comparison with the weakest baseline. This research offers a scalable framework for handling e-proctored high stakes assessments, which is secure and auditable to overcome the current limitations and advances reliable digital educational platforms.
Academic integrity and plagiarism
Intelligent Tutoring Systems and Adaptive Learning
The Monty-Hall (parameterized strategist-host) Theorem along with a constructive proof is presented, by solving the corresponding Monty-Hall Problem, wherein the host plays a parameterized strategy on the guest. It establishes the limits on the range of values for the probability of winning the prize. Eight extreme strategies (corresponding to the set of extreme values for the three perturbation parameters) have been well characterized. It is shown that there does not exist any strategy wherein a switched-choice will always (irrespective of the placement of the prize and irrespective of the initial-choice of the guest) lead to an enhancement in the chances of winning the prize. The classical Monty-Hall Problem is a special case with zero-value for each of the three perturbation parameters. This paper is an attempt to correct the errors (of long-standing historical significance) in the application of statistical methodology in solving the classical Monty-Hall Problem - one of them being the erroneous use of conditional probabilities for updating the knowledge to facilitate the decision-making by the guest, based on the information about a losing-choice, which itself is dependent on the initial-choice of the guest. Similar scenarios in data science, machine learning & artificial intelligence can have serious far-reaching consequences.
The digitalization of the architecture, engineering, and construction (AEC) industry has demonstrated the revolutionary potential of integrating blockchain technology with building information modelling (BIM). However, the selection of the most appropriate blockchain solution is a multiple-criteria decision-making (MCDM) problem, which is usually influenced by conflicting criteria and deep uncertainty. To overcome this, the present study proposes an extended Fermatean fuzzy weighted aggregated sum product assessment (Extended FF-WASPAS) model. Unlike existing Fermatean fuzzy WASPAS (FF-WASPAS) methods, which are based on a single expert and may be biased, the proposed model incorporates the evaluations of multiple decision makers (DMKs) through a consensus-driven mechanism to ensure unbiased and accurate results. A case study is conducted to evaluate five leading blockchain platforms, Hyperledger Fabric, Polkadot, Tezos, Ethereum, and Algorand, under eight BIM-related criteria. The result indicates that Ethereum is the best blockchain platform to digitalize BIM compared to the other platforms because it is scalable, interoperable, secure, and has a wide range of applications in the real world. Sensitivity analysis over a wide range of parameter values, as well as DMKs assigned with different weight sets, confirmed the stability of the ranking. Furthermore, a quantitative comparative analysis with FF multiple criteria group decision-making (FF-MCGDM) and FF-WASPAS approaches, as well as a qualitative analysis with existing models in various fuzzy environments, confirms the robustness and reliability. Overall, the study provides a strong, interpretable, and consensus-based decision-support framework with high practical value for AEC stakeholders who wish to deploy secure, transparent, and efficient blockchain-enabled BIM solutions.
R. Raja Sekar, Bhavesh Shankar Kalluru, Imran Sunkesula, Gopi Krishna Chimata · 5 authors
Blockchain mechanism, having its distributed and immutable nature, has become a revolutionary method of secure and transparent data exchange. With this potential in mind, our proposal is for Secure File Sharing System that combines AES-256&DES encryption with Ethereum smart contracts to provide a privacy-oriented and audit-ready file sharing system. The conventional centralized approach is prone to data leakages and unauthenticated access, but with the use of blockchain and encryption, this approach provides confidentiality along with trustless verification. Files are first encrypted at the server level using AES, DES symmetric encryption, thus avoiding any plaintext exposure at the server level. The file is stored encrypted within a cloud storage service called cloudinary, with sensitive metadata such as hashed file ID, destination wallet address, and unlock timestamp stored on the Ethereum blockchain through smart contracts. The blockchain layer delivers traceability and immutability with accessed file records being tamperproof. In addition, there is a time-lock mechanism in the smart contract that doesn't allow recipients to access files before the unlock time, thereby improving security and governance. The React.js frontend communicates with blockchain networks using MetaMask and ethers.js, while the Node.js and Express backend encrypts and decrypts the files, and stores metadata in MongoDB. This two-way hybrid approach effectively addresses attacks such as unauthorized access, man-in-the-middle, and data tampering. With this blend of blockchain's decentralized trust and AES's and DES's secrecy, this project lays out an innovative and viable model for secure file sharing digitally. It is robust in possible application to actual applications like enterprise document management, transfer of legal documents, and confidential communication, and provides a secure, transparent, and scalable solution.
Background The medical device sector, valued at $569 billion, faces persistent financing challenges. Around 78% of startups fail because of capital shortages, not due to lacking technical quality. Blockchain-based tokenization emerges as a way to broaden access, yet success relies on economic factors of platforms and clear regulations. Methods Transaction cost data from Bitcoin, Ethereum, and XRP Ledger covered 540 days from January 2024 to June 2025, providing 3,240 observations per network. Experts, numbering 12, participated in a modified Delphi method to form a framework tailored to healthcare. Project outcomes came from Monte Carlo simulations running 10,000 iterations, checked by a triple control-loop system, and compared against two real-world examples. Volumes of transactions drew from stochastic models involving monthly, quarterly, and annual elements, mixing fixed regulatory needs with variable market influences. Results Layer-1 (L1) fees differ by orders of magnitude; representative 2025 snapshots show BTC and ETH L1 far above XRPL and major ETH L2s. XRPL fees are typically a tiny fraction of a cent; the base cost is 10 drops (0.00001 XRP) and is dynamically adjusted by network load. Probabilities of success varied from 10.1% to 12.3% on Bitcoin, 31.4%–48.3% on Ethereum based on Layer-2 adoption, and 71.6%–73.2% on XRP Ledger. Investor involvement correlated negatively with logarithms of costs, showing Spearman <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow></mml:math> of −0.91. Differences in success exceeded 60 percentage points across platforms. Examples illustrated how elevated expenses reduce engagement in VitaDAO on Ethereum, whereas low-cost systems like XRP Healthcare support ongoing involvement. Conclusion Choosing a blockchain platform critically influences viability in tokenizing medical devices. Layer-2 options reduce cost gaps but add complexities in bridging and use. Platforms offering stability, minimal fees, and regulatory alignment promote wider inclusion and reliable funding. Technical features, steady costs, and readiness for compliance together shape whether tokenization boosts innovation in healthcare or maintains barriers.
Open access
Quality and Safety in Healthcare
Neuroethics, Human Enhancement, Biomedical Innovations
The increasing decentralization of energy generation via home solar panels and microgrids necessitates safe, scalable, and autonomous peer-to-peer (P2P) energy trading systems. Conventional grid management technologies lack the adaptability and reliability necessary for decentralized contexts. This study presents the Blockchain-Enabled Energy Swarm Protocol (BESP), which combines Ethereum smart contracts with Particle Swarm Optimization (PSO) to enhance energy trade efficiency and enable the dynamic clustering of prosumers. The protocol guarantees safe, trustless communication, low-latency energy matching, and transparent transaction auditability without dependence on a central authority. The system is assessed using empirical data from the Pecan Street Dataport dataset, which includes high-resolution records of energy usage and solar output from more than 1,000 residences in Austin, Texas. Particle Swarm Optimization (PSO) was executed in MATLAB Simulink, whilst smart contracts were deployed and evaluated via Remix IDE and Ganache on a private Ethereum network. Experimental findings indicate that BESP decreases transaction latency by 35.2%, reduces communication overhead by 27.8%, and enhances energy cost efficiency by more than 60% relative to traditional P2P and centralized frameworks. These findings underscore BESP's efficacy in facilitating energy-efficient, secure, and decentralized communications inside smart grids, in accordance with future sustainable infrastructure objectives.
This chapter presents a comprehensive exploration of the evolving landscape of financial technology (FinTech), highlighting the transformative role of innovations such as digitization, blockchain, artificial intelligence (AI), and data analytics. It traces the historical evolution of FinTech, from early electronic banking systems to the advent of decentralized finance and mobile payment solutions. The current FinTech ecosystem is examined through the lens of technological convergence, regulatory evolution, digital payment adoption, and the rise of ESG investing. Emerging trends, including open banking, robo-advisors, and RegTech, are discussed alongside the sector’s pivotal challenges—ranging from data privacy to regulatory compliance and talent acquisition. The chapter also identifies key opportunities for collaboration and experimentation through ecosystem partnerships and regulatory sandboxes. 78 Looking ahead, it forecasts continued disruption, embedded finance growth, and the expanding impact of FinTech on financial inclusion and sustainability. This forward-looking analysis underscores the sector’s potential to reshape global financial systems while emphasizing the need for adaptive strategies, robust governance, and inclusive innovation.
Slow and disputed progress payments undermine contractor liquidity and project schedules due to manual verification, fragmented data, and limited transparency. This paper presents a prototype system that incrementally integrates digital twin (DT), building information modeling (BIM), and blockchain to automate milestone-based payments. The continuously updated DT is conceptualized as a dynamic oracle, capturing real-time site conditions and comparing them with structured BIM milestones. Verified achievements trigger Ethereum smart contracts referencing Merkle-proofed evidence stored off-chain in InterPlanetary File System (IPFS), with disbursements authorized via Gnosis Safe multi-signature wallets. A prototype on a police station project shortened verification to payment from several days to minutes and eliminated disputes across all milestones. A survey of industry professionals confirmed gains in efficiency, transparency, and trust. The proposed system provides a practical foundation for transparent, automated payments and offers pathways for future adoption such as stablecoin settlement and public sector integration. • Digital twins resolve blockchain oracle challenges in construction payments. • BIM-blockchain integration reduces payment verification from days to minutes. • Smart contracts with multi-signature governance secure transaction integrity. • Stakeholders confirm enhanced payment speed, transparency, and trust.
SivaKumar Depuru, Obulareddy Chandana, M. Prakash, Nimsivardhan · 5 authors
Internet of Vehicles (IoV) needs to have effective security and privacy models that can be relied upon to deliver reliable information transfer between vehicles, roadside facilities, and cloud offerings. Although the newly suggested Quantum-Inspired Metaheuristic Framework (QIMF) is a useful tool in terms of trust assessment, adaptive privacy contracts, and secure access, its centralized architecture introduces the issues of scalability, auditing, and collusion/tampering resilience. To address these drawbacks, the present research proposes a framework using BQIMF, a Blockchain-Based Quantum-Inspired Metaheuristic Framework that combines quantum-inspired optimization with distributed ledger technology in decentralized trust maintenance and privacy protection. The model presented proposes that QIMF calculates trust scores and signs privacy agreements by quantum superposition and tunneling, and smart contracts based on blockchain are used in a way that the agreements and access decisions between the IoV ecosystem are recorded permanently. This hybrid solution removes points of failure, facilitates audit trails that cannot be tampered with and allows compliance with privacy policies to be verified. The BQIMF is tested over the CICIoV2024 dataset in various vehicular communication conditions, including denial-of-service, spoofing, and man-in-the-middle attacks in order to validate performance. As demonstrated in experiments, BQIMF has 98.7% accuracy in trust evaluation, 35% lower unauthorized access rate than classical blockchain-only systems, and a low access latency (under 25 ms) despite large network load. These results indicate that blockchain and quantum-inspired optimization have the potential to deliver a scalable, decentralized, and adaptable solution to secure vehicular communication, leading to the deployment of next-generation IoV.
Sabri Barbaria, Abderrazak Jemai, Halil İbrahim Ceylan, Raul Ioan Muntean · 6 authors
Background: Healthcare data interoperability faces significant barriers, including regulatory compliance complexities, institutional trust deficits, and technical integration challenges. Current centralized architectures demonstrate inadequate mechanisms for balancing data accessibility requirements with patient privacy protection, as mandated by HIPAA and GDPR frameworks. Traditional compliance approaches rely on manual policy implementation and periodic auditing, which are insufficient for dynamic, multi-organizational healthcare data-sharing scenarios. Objective: This study develops and proposes a blockchain-based healthcare data management framework that leverages Hyperledger Fabric, IPFS, and the HL7 FHIR standard and incorporates automated regulatory compliance mechanisms via smart contract implementation to meet HIPAA and GDPR requirements. It assesses the theoretical system architecture, security characteristics, and scalability considerations. Methods: We developed a permissioned blockchain architecture that employs smart contracts for privacy policy enforcement and for patient consent management. The proposed system incorporates multiple certification authorities for patients, hospitals, and research facilities. Architectural evaluation uses theoretical modeling and system design analysis to assess a system’s security, compliance, and scalability. Results: The proposed framework demonstrated enhanced security through decentralized control mechanisms and cryptographic protection protocols. Smart contract-based compliance verification can automate routine regulatory tasks while maintaining human oversight in complex scenarios. The architecture supports multi-organizational collaboration with attribute-based access control and comprehensive audit-trail capabilities. Conclusions: Blockchain-based healthcare data-sharing systems provide enhanced security and decentralized control compared with traditional architectures. The proposed framework offers a promising solution for automating regulatory compliance. However, implementation considerations—including organizational readiness, technical complexity, and scalability requirements—must be addressed for practical deployment in healthcare settings.
Purpose: This paper formulates a new theoretical framework to address the principal-agent problem in representative democracy through a dynamic voting mechanism. Based on Rousseau’s concept of the general will and contemporary analyses of corporate influence in politics, I build a rigorous mathematical model that enables voters to maintain continuous oversight over their elected representatives. Design/methodology/approach: I developed a rigorous mathematical model integrating an anonymous blockchain-based voting system. This system allows voters or voter groups to continuously monitor their representatives while preserving their privacy through zero-knowledge proofs. The model uses game theory and extends Condorcet’s Jury Theorem to analyze voter behavior under dynamic oversight conditions. Findings: The results show that such a system can encourage a more responsible form of representative democracy while maintaining electoral stability. Detailed implementation architectures show that the model is not only theoretically rigorous but also practically feasible through advanced cryptographic tools. Practical implications: The proposed architecture enables real-time voter engagement without compromising privacy, providing a blueprint for secure, transparent, and scalable voting systems applicable in modern democratic systems. Originality/value: This research combines political theory, cryptographic system design, and social choice theory to propose a new paradigm for democratic governance. The integration of zero-knowledge proofs with dynamic feedback mechanisms offers a scalable solution to fundamental challenges of voter privacy and election integrity, with far-reaching implications for democratic theory and its practical application.
María de la O González, Francisco Jareño, María Caridad Sevillano
Purpose This study aims to examine how cryptocurrency returns – specifically Bitcoin, Cardano and Tether – respond to unexpected shocks in inflation and interest rates and assess their potential as hedge, safe-haven or diversifier assets against them, comparing them to gold, the traditional safe-haven asset. Design/methodology/approach The research spans two sub-periods (2019–2021 with stable interest rates and 2022–2024 with rising rates) and uses quantile regression to capture the distribution of returns across market conditions. Findings The main findings of this study reveal that, first, Tether shows a consistently negative and statistically significant relationship with both nominal and real interest rates during bull markets, evidencing Tether’s role as a hedge asset against interest rates. Second, Tether together with Cardano throughout the full period and the second sub-period of interest rate hikes, as well as with Bitcoin during the first sub-period could be taken into account by investors to diversify nominal interest rate risk. Third, gold consistently shows a positive and statistically significant relationship with shocks in inflation expectations during economic recessions, suggesting its role as a hedge or even a safe-haven against inflation. Fourth, Bitcoin emerges as a potential safe-haven against inflation in the second subperiod, characterised by an upward trajectory in interest rates and driven in part by inflationary pressures arising from the Russia–Ukraine conflict. Research limitations/implications Future research could explore the impact of government regulation on the adoption and performance of cryptocurrencies, as well as the relationship between cryptocurrencies and other financial markets. Investigating the behavioural aspects of cryptocurrency investors, the environmental impact of green cryptocurrencies and the adoption of cryptocurrencies in emerging markets are also promising areas of research. Practical implications The results underscore the diverse responses of cryptocurrencies to macroeconomic factors, highlighting their role as a portfolio diversifier, hedge or safe-haven asset and suggesting further research into regulatory implications. Therefore, our findings have significant economic implications, particularly for portfolio management and investment strategies. The study shows that including a mix of traditional, green and stable cryptocurrencies can improve portfolio diversification and mitigate risks associated with interest rate and inflation fluctuations. Social implications This research can provide valuable insights for investors and policymakers, helping them to better understand and manage cryptocurrency investments. Policymakers can use our findings to develop regulations that support the adoption of cryptocurrencies while mitigating legal and operational risks. For example, understanding the different roles of different types of cryptocurrencies in hedging against economic variables can inform regulatory decisions that promote financial stability and protect investors. In addition, our study highlights the importance of educating retail investors on the benefits of diversifying their holdings with a mix of cryptocurrencies and traditional assets such as gold. Financial analysts and market participants can use these insights to provide better market analysis and educational resources, helping investors make informed decisions and fostering a more resilient financial ecosystem. Originality/value For market participants, the study highlights the importance of including a mix of traditional, green and stable cryptocurrencies to improve portfolio diversification, especially in times of economic uncertainty. Portfolio managers can use cryptocurrencies such as Tether and gold to hedge against interest rate and inflation risks, respectively, while retail investors should be educated on diversifying their holdings by combining different sorts of cryptocurrencies and gold. Even Bitcoin is emerging as a safe-haven against inflation in times of rising interest rates and inflationary pressures. Institutional investors can develop strategic asset allocation models that include cryptocurrencies and ensure regulatory compliance to mitigate legal and operational risks. Policymakers should create clear regulatory frameworks that balance innovation with investor protection, and financial analysts can provide market analysis and educational resources to help investors make informed decisions.
AbstractAs a key bridge between the real economy and financial capital, supply chain finance generates core data such as transaction documents, logistics information, and financing contracts, whose secure, trustworthy, and controllable management is crucial.Traditional centralized notarization schemes suffer from single points of failure, risks of data tampering, and high trust costs.Although blockchain offers tamper-evident notarization, limited on-chain storage and throughput constrain its direct use in large-scale data scenarios.To address this tension, this paper investigates a secure data notarization and access control algorithm grounded in an on-chain/off-chain hybrid storage architecture and smart contracts.We first construct a layered data management model: high-value, low-volume data hashes (digital fingerprints) and key access-control policies are anchored on-chain to ensure immutability, while complete large-volume raw data are encrypted and stored off-chain (e.g., in IPFS or distributed databases) to ensure scalability.To tackle potential challenges of on-/off-chain consistency and integrity verification under this hybrid architecture, we design an efficient verification mechanism based on cryptographic commitments, ensuring any tampering with off-chain data can be detected quickly and succinctly.Furthermore, to achieve fine-grained privacy protection and compliant use, we propose a smart-contract-based dynamic access control algorithm.By deploying access-control policies as executable code on-chain, the algorithm performs automated logical checks to deliver precise authorization and comprehensive audit logging, ensuring security and transparency throughout data sharing and circulation.Through theoretical security analysis and prototype experiments, the proposed scheme preserves data immutability and traceability while significantly improving the storage efficiency and processing performance of supply chain finance notarization systems, and it enables flexible and secure access control.The results indicate that the coordinated mechanism of on-chain/off-chain hybrid storage and smart contracts offers a feasible technical pathway for building efficient, trustworthy, and secure supply chain finance infrastructure.