With the standardization of the logistics market and advancements in innovation, trust issues arising from information asymmetry among supply chain participants have become increasingly prominent. This paper examines a blockchain-enabled collaborative regulatory system for logistics service supply chains involving the government, logistics enterprises, and the logistics market. Using evolutionary game theory, a three-party evolutionary game model is constructed and validated through system dynamics simulations to explore the impacts of various factors on the collaborative regulatory system. The results indicate that, during the system’s evolution, logistics enterprises stabilize first, followed by the government, with the logistics market converging the slowest. The added value of logistics services is identified as the core factor driving logistics enterprises to adopt blockchain, exhibiting a significantly stronger impact compared to regulatory benefits or improvements in quality and safety. While robust incentive policies can rapidly increase enterprises’ willingness to adopt blockchain technology, they concurrently weaken the government’s enthusiasm for regulation.
With the explosive growth of online education resources, traditional education platforms that rely on centralized servers for resource distribution and storage gradually expose issues such as inefficient resource management, lack of trust in sharing, high storage costs, and a high risk of single-point failure. In response, this study designs a decentralized education resource sharing platform by integrating blockchain technology and distributed file systems. It leverages the distributed ledger, immutability, and traceability features of blockchain, along with the high data availability and low storage cost advantages of distributed file systems. Results show that the proposed education platform reaches 1,388 transactions per second when the number of nodes is 200, with a latency response time of 9.8 seconds and an average memory overhead of 268 MB. In practical performance evaluation, the platform achieves a top-10 hit rate of 97.2%, a business interruption probability as low as 4.8% under unexpected conditions, and an average resource throughput efficiency of 176 Mbps. Overall, the platform performs well in education resource sharing and demonstrates strong algorithm fault tolerance, practicality, robustness, and service stability, providing reliable technical support for global education resource sharing.
This paper proposes a novel decentralized blockchain verification system utilizing distributed Bayesian Networks (BNs). Traditional blockchain verification relies heavily on cryptographic proofs, which can be computationally intensive and susceptible to specialized attacks. Our approach offers a probabilistic and decentralized alternative. Each node maintains a Bayesian Network representing the blockchain's transaction graph, continuously updated with observed transactions. Consensus is achieved through iterative Bayesian inference and probabilistic agreement on the validity of new transactions. This system mitigates single points of failure, enhances security through probabilistic reasoning, and provides a more scalable verification process compared to traditional methods. The core claim is that a decentralized blockchain verification system can be built by leveraging distributed Bayesian Networks to model and verify transaction dependencies. The core mechanism involves continuous BN updates and consensus through iterative inference. This paper outlines the system architecture, the probabilistic inference process, and discusses potential applications and future research directions.
Blockchain technology relies heavily on consensus protocols to ensure data integrity and security. However, the decentralized and often complex nature of these protocols makes formal analysis and design challenging. This paper proposes a novel approach to formally specifying and analyzing blockchain consensus protocols using game theory. We model the consensus process as a strategic game, considering the incentives of different participants and deriving the resulting equilibria. This framework allows for a rigorous assessment of protocol design, identifying vulnerabilities and potentially optimizing performance. The core claim is that game theory provides a viable tool for both designing and analyzing blockchain consensus protocols. We explore various consensus mechanisms, including Proof-of-Work and Proof-of-Stake, demonstrating the application of our method. The key contribution is a theoretical framework offering a systematic approach to blockchain consensus design, moving beyond intuitive assumptions and enabling a more robust and secure system.
Introduction Shippers may undeclare maritime dangerous goods as general cargo to avoid the preparation time, documentation burden, and freight premium associated with dangerous goods transportation, creating serious risks for vessel safety, port operations, and the marine environment. This paper examines whether blockchain-enabled documentation can mitigate such undeclaration by improving provenance information and shortening preparation and verification time in the dangerous goods channel. Methods We develop a game-theoretic model with one carrier and a continuum of heterogeneous shippers who differ in their marginal willingness to pay for service level, and in which the carrier endogenously sets the dangerous goods transportation price. We characterize the equilibrium declaration behavior under a benchmark scenario and under blockchain adoption, and extend the model to settings with carrier competition and enhanced detection. Results Undeclaration motives vary with the service environment: when dangerous goods service is relatively low, service-sensitive shippers undeclare to access the faster generalcargo channel; when service is sufficiently high, price-sensitive shippers undeclare to avoid the dangerous goods tariff. The carrier’s profit-maximizing price therefore does not generally coincide with the regulatory objective of zero undeclaration. Blockchain adoption introduces two opposing forces: a service-enhancement effect that encourages truthful declaration and a cost-escalation effect that raises the dangerous goods price. As a result, blockchain reduces undeclaration only when documentation-time savings dominate the price premium induced by adoption costs; when this condition fails, adoption may increase undeclaration and reduce compliant shipper surplus. Discussion Blockchain-enabled documentation is not a universal safety remedy. Its effect on undeclaration depends on the interaction between documentation-time savings and the adoption-driven price increase, and the extensions with carrier competition and enhanced detection show that blockchain deployment should be evaluated jointly with pricing, detection, competition, and regulatory policies.
Blockchain technology's core functionality relies heavily on consensus algorithms to maintain data integrity and security. However, the complexity inherent in these algorithms introduces significant potential for errors and vulnerabilities. This paper proposes a formal verification approach utilizing model checking to rigorously assess the correctness and security of prominent blockchain consensus algorithms, including Proof-of-Work (PoW) and Proof-of-Stake (PoS). We define formal specifications of these algorithms and employ a model checker to explore all possible states and transitions, identifying potential bugs and ensuring adherence to protocol rules. The methodology presented offers a systematic and automated means of guaranteeing the reliability of blockchain systems, a critical step towards wider adoption and trust. This work focuses on the theoretical aspects of verification, providing a framework for future practical implementation and integration within blockchain development workflows. The key contributions are a detailed specification language for blockchain algorithms and a demonstrated application of model checking to uncover subtle vulnerabilities.
This paper proposes a novel decentralized verification protocol for distributed systems leveraging blockchain technology and cryptographic commitments. The core idea is to eliminate the need for a central authority by enabling components to independently verify each other's outputs through a trustless and auditable process. The system utilizes smart contracts on a blockchain to record component outputs and their corresponding commitments, establishing a verifiable record of the system's behavior. This approach offers a fundamentally new method for distributed systems security, addressing limitations inherent in traditional centralized verification models. The protocol's key components include commitment schemes, decentralized consensus mechanisms, and blockchain-based storage, all designed to ensure the integrity and authenticity of distributed system components. We outline the protocol's architecture, detailing the cryptographic operations and blockchain interactions involved. The resulting system provides a robust and scalable solution for verifying distributed systems, particularly in scenarios where trust is limited or absent.
This paper proposes a novel system for program code version control leveraging the principles of blockchain technology. Traditional version control systems are vulnerable to manipulation and security breaches, necessitating a more robust and transparent solution. Our system utilizes blockchain's inherent properties – immutability and distributed consensus – to provide a highly secure and auditable record of code changes. The core mechanism involves hashing each code version and storing the hash on a blockchain, ensuring that any alteration to the code will be immediately detectable. This approach significantly enhances the integrity of the codebase and promotes trust among developers and stakeholders. The system is designed for flexibility and scalability, adaptable to various programming languages and development workflows. This paper outlines the architecture, key features, and theoretical underpinnings of the proposed system, emphasizing its advantages over existing methods.
This paper proposes a Blockchain-Based Distributed Data Verification System (BBDVS) designed to address the inherent trust issues present in traditional distributed data verification methods. The system leverages the core principles of blockchain technology – namely, its consensus mechanisms and immutability – to provide a secure, transparent, and verifiable record of data integrity. BBDVS utilizes a distributed ledger to maintain a chronological and tamper-proof audit trail of data transactions. Each transaction, representing a data verification event, is cryptographically linked to the previous one, forming a chain. The system employs a consensus mechanism to validate transactions and add them to the blockchain, ensuring data integrity and preventing malicious alterations. This approach eliminates the need for a central authority, reducing single points of failure and enhancing overall system resilience. The paper details the architecture and operational aspects of the BBDVS, focusing on the key components and their interactions. We explore the potential applications of the system across various domains where data integrity and trust are paramount.
This paper proposes a novel approach to governing distributed machine learning (ML) models using blockchain technology. The core claim is to establish a decentralized platform for managing ML model versions, controlling access permissions, and distributing rewards, all while enhancing transparency and trust. The proposed mechanism leverages blockchain's immutability and smart contract capabilities to record model metadata, training data provenance, and participant information. This allows for automated execution of governance rules, mitigating issues associated with traditional, centralized ML model management, such as single points of failure, biased data handling, and lack of transparency. The system aims to foster a more equitable and trustworthy environment for collaborative ML development and deployment. Key performance metrics, such as model accuracy, data integrity, and participant engagement, are inherently tracked and verifiable through the blockchain.
This paper explores the application of blockchain technology to manage distributed edge computing resources. The core claim is that blockchain can facilitate dynamic resource allocation and efficient utilization within edge computing environments. The proposed mechanism involves constructing a blockchain-based resource management system leveraging smart contracts to automate and optimize resource distribution. This approach addresses the challenges of centralized control, inefficient resource utilization, and security vulnerabilities commonly found in traditional edge computing models. The research investigates the potential benefits of blockchain's decentralized, transparent, and immutable ledger for enhancing edge computing resource management, ultimately leading to improved performance, scalability, and trust within distributed edge systems. The key focus is on establishing a secure and automated framework for resource sharing and access control, significantly improving the overall efficiency and reliability of edge computing deployments. ---
This paper proposes a novel approach to mathematical proof verification utilizing blockchain technology and distributed consensus mechanisms. Traditional proof verification relies on centralized authorities, creating potential vulnerabilities related to trust, manipulation, and single points of failure. Our system addresses these concerns by representing proof steps as transactions on a blockchain. Consensus mechanisms, such as Proof-of-Work or Proof-of-Stake, are employed to validate and secure the proof process, ensuring its integrity and immutability. The core claim is that the correctness of mathematical proofs can be verified through a distributed system leveraging blockchain consensus mechanisms. This approach offers increased transparency, auditability, and resistance to fraud, fundamentally changing the landscape of mathematical verification. We detail the architecture, transaction structure, and consensus protocol design, outlining a robust framework for distributed proof verification. The system's potential impact extends beyond individual proofs, offering a foundation for collaborative mathematical research and a verifiable record of mathematical discoveries. We define the key mathematical components and the associated notations used throughout this document.
This paper proposes a novel approach to software supply chain security management leveraging the inherent characteristics of blockchain technology. The core claim is to build a robust management system capable of guaranteeing the integrity and traceability of software components throughout their lifecycle. The proposed mechanism utilizes a blockchain network to record critical data points related to the software supply chain, including code commits, build processes, and security audits. Smart contracts are then employed to automate security checks, enforce access control, and trigger alerts based on predefined rules. This approach addresses the escalating risks associated with compromised software supply chains by providing an immutable and auditable record of all activities. The research highlights the potential of blockchain to significantly enhance software security and trust within complex, distributed development environments. The key contribution lies in the systematic application of blockchain and smart contracts specifically tailored for supply chain security, offering a verifiable and resilient solution.
This paper proposes a novel approach to software version control leveraging the inherent properties of blockchain technology. Traditional version control systems suffer from centralized vulnerabilities and single points of failure, leading to potential data loss and compromised integrity. This system addresses these limitations by utilizing blockchain's immutable ledger and distributed storage capabilities. Each software code version is recorded as a transaction on the blockchain, secured by a consensus mechanism. This ensures a complete and verifiable history of code changes, dramatically enhancing security, reliability, and transparency compared to conventional methods. The core claim of this work is that the combination of blockchain's features provides a significantly more robust and trustworthy software version control solution. The system's design incorporates key mechanisms such as transaction hashing, smart contracts for version management, and a distributed consensus protocol to guarantee data integrity and consistency. The resulting architecture offers a compelling alternative for organizations seeking a secure and resilient software development environment.
Blockchain technology relies fundamentally on consensus mechanisms to ensure data integrity and prevent fraud. However, the inherent complexity of these mechanisms often leads to subtle vulnerabilities that can be exploited. This paper presents a novel approach to blockchain security by developing a formal specification language and accompanying verification tools. We aim to rigorously analyze and verify the security and performance of various blockchain consensus protocols, including Proof-of-Work (PoW) and Proof-of-Stake (PoS). The methodology employs mathematical modeling and logical reasoning to identify potential weaknesses and assess protocol robustness. The developed tools facilitate a systematic examination of protocol behavior under various conditions, ultimately leading to the design of more secure and reliable decentralized systems. This work offers a significant advancement in the field by providing a concrete framework for formal verification, moving beyond anecdotal evidence and subjective assessments. The core claim of this paper is that blockchain consensus mechanisms are complex and prone to vulnerabilities, and the proposed approach provides a mechanism to address this issue.
In today’s fast changing digital world, the need for secure, transparent, and reliable financial transactions is more important than ever especially in areas where fraud, delays, and unauthorized access are common concerns. Traditional payment systems often depend on centralized middlemen, which can lead to slow processing, high fees, and risks of data tampering or cyberattacks. This work introduces an automated payment processing system powered by blockchain technology, designed to make digital transactions faster, safer, and more trustworthy without relying on third parties. The motivation for this system came from real-world frustrations with issues like payment fraud, slow transactions, and the lack of visibility in how money moves within traditional financial systems. To build this system effectively, the Structured Systems Analysis and Design Methodology (SSADM) was adopted. This method provides a clear, step-bystep approach for understanding problems and creating effective systems. With blockchain at its core, the system will support real-time transaction validation, ensure that data can’t be altered, reduce costs, and remove central points of failure. Overall, it aims to build user confidence and create a more resilient payment infrastructure. By solving key problems found in conventional systems, this project hopes to contribute to the next generation of secure, scalable, and efficient financial technologies for businesses and organizations.
Trust is a fundamental element underpinning the successful operation of blockchain networks, yet it is frequently treated as an inherent characteristic rather than a subject of explicit investigation. This paper presents a novel formal model of trust within blockchain networks, leveraging game theory and network topology to provide a rigorous analytical framework. The model, denoted as (N, E, V, T), describes a network of nodes (N) connected by edges (E), each node possessing a valuation (V) and a trust threshold (T). Trust is modeled as a dynamic process influenced by node interactions, reputation, and network structure. The core contribution lies in defining the trust propagation mechanism, which can be expressed as: *Trust(i, j) = Trust(i, j) + α * (r(i, j) - T(i))* where: * *Trust(i, j)* represents the trust level between node *i* and node *j*. * *Trust(i, j)* represents the current trust level between node *i* and node *j*. * *α* is a trust propagation coefficient (0 ≤ *α* ≤ 1). * *r(i, j)* is the reputation score of node *j* as perceived by node *i*. * *T(i)* is the trust threshold of node *i*. This equation illustrates that trust between two nodes is influenced by the difference between the node's perceived reputation of the other node and its own trust threshold. The model allows for the simulation of various blockchain scenarios, including Byzantine fault tolerance, Sybil attacks, and collusion, providing valuable insights for designing robust and trustworthy blockchain systems. Furthermore, the model facilitates the exploration of trust-enhancing mechanisms, such as reputation systems, staking mechanisms, and consensus algorithms, by quantifying their impact on trust dynamics. The research contributes to a deeper understanding of the complexities of trust in distributed ledger technologies and offers a practical tool for improving their security and efficiency. ---
This paper proposes a novel approach to distributed trusted computation leveraging the inherent properties of blockchain technology. The core claim is that blockchain's consensus mechanisms and data integrity guarantees can facilitate a secure and trustworthy distributed computing environment, effectively addressing the trust issues prevalent in cloud computing. The proposed mechanism involves decomposing computational tasks into smaller sub-tasks, which are then collaboratively executed by nodes within a blockchain network. Smart contracts are employed to manage task scheduling and validate the results. This system offers an alternative to traditional trust models, utilizing cryptographic techniques and distributed consensus for enhanced security and transparency. The research explores the potential of blockchain to fundamentally transform the landscape of distributed computing, providing a robust solution for sensitive computations and data processing. The paper focuses on the technical design and theoretical underpinnings of this approach, outlining key components and potential challenges.
The paper provides an integrated literature review of recent scientific publications on quantum computing in finance and identifies promising directions for future research on the subject. The review covers seven thematic areas: portfolio optimization, derivative pricing and stochastic volatility, quantum machine learning for fraud detection and credit risk, insurance and actuarial science, mixed-frequency econometrics, fuzzy-quantum approaches for financial explainability, and security of cryptocurrencies. The paper compiles the essential quantum computational methods proposed in the literature, outlines their economic significance and the existing constraints for empirical testing and implementation, and discusses cross-cutting issues of explainability, trustworthy AI, robustness, and governance that arise across these application domains. Drawing on this review, the paper identifies five macro-gaps in the existing literature and proposes seven concrete directions for future research, grounded in European financial data and currently available quantum computing infrastructure. A special focus throughout is the increasingly available quantum infrastructure in Europe and the regulatory emphasis on trustworthy artificial intelligence, both of which create timely opportunities for future applications in financial modelling, risk management, and explainable financial AI.
In cryptocurrency markets, credibility may be created before it can be verified. Promotional signals from influencers, online communities, exchanges, and project actors can create an impression of legitimacy before consumers are able to independently assess the quality, risks, or underlying value of the asset. This systematic review examines how promotional activity contributes to this condition and how the literature connects it with ethical concerns, consumer consequences, and governance. Following PRISMA 2020, 54 empirical studies published between 2019 and 2025 were identified through Scopus and analyzed using thematic synthesis. The findings indicate that promotion is distributed across social media, influencers, communities, exchanges, and project actors, making the boundary between marketing, personal opinion, and financial advice difficult to maintain. Ethical problems arise not only from fraud, but also from selective information, hidden incentives, artificial attention, and market signals that give uncertain assets an appearance of legitimacy. Consumer vulnerability is similarly situational. Knowledge and experience may offer some protection, but trust, technological complexity, social influence, and speculative expectations continue to shape judgement. Regulatory and educational responses remain fragmented because responsibility is dispersed across actors and jurisdictions. The review connects these relationships through an ethical marketing cycle that conceptually organizes how ethical concerns may develop across communication, market activity, consumer interpretation, and governance. This perspective extends ethical marketing beyond the accuracy of individual claims and locates responsibility within the structures through which cryptocurrency credibility is produced. The review is limited by its reliance on Scopus and the absence of a formal risk-of-bias assessment of the included studies.
This study evaluates the risk-adjusted consequences of large-weight equity–cryptocurrency substitution and examines whether these effects differ systematically across equity styles (Growth vs. Value) and regions (Asia, Europe, and the Americas). Using daily data from January 1, 2021 to December 31, 2023, the analysis constructs style-segmented MSCI country equity indices and compares annual Sharpe ratios under four constant-mix, daily rebalanced strategies: (S1) 100% equity; (S2) 50% equity / 50% cryptocurrency; (S3) 50% equity / 50% global bonds; and (S4) 33.33% equity / 33.33% global bonds / 33.33% cryptocurrency. Five major non-stablecoin cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), and Binance Coin (BNB)—are evaluated individually to isolate coin-specific substitution effects. Performance is assessed annually and compared across Growth and Value portfolios within identical country–year–cryptocurrency environments to identify style-dependent outcomes. The results show that cryptocurrency substitution generally improves Sharpe ratios, but the effects are benchmark-, coin-, style-, and region-dependent. Improvements are more heterogeneous under direct 50% equity–crypto substitution, especially for Value portfolios, but become uniformly positive when crypto is introduced within an equity–bond benchmark. SOL provides the largest and most consistent improvements, while ETH and BNB are frequently strong and BTC is the least consistent. Growth portfolios benefit more than Value portfolios in Asia and the Americas, whereas Europe shows more style-neutral effects. Because cryptocurrency shocks are common within a given coin-year, statistical inference is interpreted as cross-market evidence rather than fully independent observations.
Abstract This study examines the dynamic volatility spillover between financial technologies and traditional and alternative financial markets. The analysis utilizes daily data covering the period from January 2018 to March 2026 for FinTech ETFs, the Nasdaq, Bitcoin, and gold markets. Interconnectivity among financial markets was analyzed using the time-varying parameter VAR (TVP-VAR) connectedness approach. The findings indicate the presence of a moderate and time-varying connectivity structure among the markets. In particular, a strong interaction was observed between the FinTech and Nasdaq markets, while Bitcoin was found to play a significant role as a volatility transmitter during certain periods. The gold market, on the other hand, generally exhibited more stable and limited interactions. Additionally, the study found that financial market linkages increase during periods of crisis and uncertainty. These results highlight that the transmission of volatility across financial markets has a dynamic structure and that portfolio diversification strategies should be evaluated accordingly. By examining FinTech markets alongside other major asset classes, the study provides a timely and comprehensive contribution to the literature.
Bitcoin has become an increasingly important asset for portfolio allocation, yet its diversification value and option-implied information remain difficult to evaluate. This paper examines Bitcoin risk from portfolio and option-implied perspectives. This study assesses whether Bitcoin improves the risk-return opportunity set with traditional assets and whether its diversification role remains stable during market stress. Option-implied measures, including the 25-delta Risk Reversal (RR25), smile curvature, and an at-the-money Implied-Volatility-minus-Realized-Volatility (IV-minus-RV) proxy, are then constructed to predict market conditions. Portfolio analysis shows that Bitcoin can improve risk-return tradeoffs but does not function as a stable minimum-variance asset or a reliable crisis hedge. Baseline regressions provide limited evidence that RR25 consistently predicts future realized volatility or returns. However, extreme negative short-dated RR25 is followed by higher future realized volatility, suggesting RR25 is more informative as a nonlinear stress-state indicator than as a continuous forecasting variable. Smile curvature captures the implied-volatility surface but provides weaker predictive information. Finally, the IV-minus-RV analysis shows that gradual RR25-based exposure scaling achieves a better risk-adjusted profile than a binary exposure rule. Overall, the findings indicate that Bitcoin's diversification benefits and option-implied information are state-dependent.
Abstract Financial technologies (Fintech), such as digital payments, have become transformative economic tools. Yet despite technological advances and the documented benefits of financial inclusion, 1.3 billion adults remained unbanked in 2024, and cash persists globally. Why is fintech growth accompanied not by more intermediation but by persistent disintermediation (through cash and, increasingly, Bitcoin) that varies significantly across countries? I present a theory of disintermediation identifying three primary drivers: weak state capacity, underdeveloped infrastructure, and political institutions shaping citizens’ incentives regarding formal finance. The first two are supply-side factors: weak state capacity enables merchants to demand cash payments to avoid taxation, strengthening informal sectors, and lacking banking infrastructure raises the cost of intermediation. The third is a demand-side factor extending Hirschman’s ‘Exit, Voice, and Loyalty’ framework to finance: autocratic governance increases citizens’ exit from formal finance. I test this theory through two empirical analyses using two-way fixed effects, each capturing disintermediation within a different population: First, cash dependency among the broad population of economic actors in 158 countries, 2001–2020 ( n = 2760). Second, the choice of peer-to-peer over exchange-based channels among cryptocurrency users in 161 countries, 2019–2024 ( n = 921), using a novel dataset provided by Chainalysis, a market leader in blockchain intelligence. The two measures are deliberately not parallel: the cash analysis tests the theory on the broadest possible population, while the Bitcoin analysis tests whether the same institutional drivers predict the choice of disintermediated channels within the population of cryptocurrency users. Consistent results across populations this different indicate that the theorized mechanism is general rather than an artifact of either measure. Results are robust across estimators, including Callaway and Sant’Anna staggered difference-in-differences. Findings demonstrate that supply and demand drivers each shape disintermediation, and establish a research agenda investigating fintech adoption through financial disintermediation.