Andrew Isaak, Baris Istipliler, Suleika Bort, Michael Woywode
Decentralized autonomous organizations (DAOs) represent a novel organizational form enabling self-governed coordination based on blockchain technology. This study examines the prototypical Bitcoin DAO from an institutional perspective, focusing on how its core features—decentralization and autonomy—interact with the broader institutional framework in which it operates. Specifically, we study how regulative institutional environments (i.e., (il)legalization) shape the growth and development of DAOs while theorizing about the role of both petty and grand corruption (i.e., by higher-level officials) in influencing the effectiveness of these regulative institutions. Our empirical analysis focuses on the global rise of Bitcoin trading and platform establishment across 49 national contexts from 2011 to 2023. Utilizing a unique data set, we find that, although the number of Bitcoin exchange platforms in a country is positively associated with Bitcoin legalization, Bitcoin trading volume is positively associated with Bitcoin illegalization. In countries with higher levels of grand corruption, Bitcoin illegalization becomes even more strongly associated with trading. In contrast, grand corruption dampens the positive association between legalization and the number of Bitcoin exchange platforms. Further, the presence of petty corruption reduces the impact of grand corruption. Our study reveals that it is critical to distinguish between petty and grand corruption as an important factor that influences the interplay between the regulative environment and growth and development of the Bitcoin DAO and the related ecosystem of Bitcoin trading and platform founding.
Md. Shariful Islam, Mohammad Saifur Rahman, M. Sohel Rahman
Log management is crucial for ensuring the security, integrity, and compliance of modern information systems. Traditional log management solutions face challenges in achieving tamper-proofing, scalability, and real-time processing in distributed environments. This paper presents a blockchain-based log management framework that addresses these limitations by leveraging blockchain's decentralized, immutable, and transparent features. The framework integrates a hybrid on-chain and off-chain storage model, combining blockchain's integrity guarantees with the scalability of distributed storage solutions like IPFS. Smart contracts automate log validation and access control, while cryptographic techniques ensure privacy and confidentiality. With a focus on real-time log processing, the framework is designed to handle the high-volume log generation typical in large-scale systems, such as data centers and network infrastructure. Performance evaluations demonstrate the framework's scalability, low latency, and ability to manage millions of log entries while maintaining strong security guarantees. Additionally, the paper discusses challenges like blockchain storage overhead and energy consumption, offering insights for enhancing future systems.
Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification techniques. We propose a multi-dimensional method integrating fund flow analysis, behavioral similarity, and anomalous transaction detection to identify related addresses. We develop a comprehensive set of liquidity risk indicators tailored for meme tokens, covering token distribution, trading activity, and liquidity metrics. Empirical analysis of tokens like BabyBonk, NMT, and BonkFork validates our approach, revealing significant disparities between apparent and actual liquidity in meme token markets. The findings of this study provide significant empirical evidence for market participants and regulatory authorities, laying a theoretical foundation for building a more transparent and robust meme token ecosystem.
Blockchain transaction data exhibits high dimensionality, noise, and intricate feature entanglement, presenting significant challenges for traditional clustering algorithms. In this study, we conduct a comparative analysis of three clustering approaches: (1) Classical K-Means Clustering, applied to pre-processed feature representations; (2) Hybrid Clustering, wherein classical features are enhanced with quantum random features extracted using randomly initialized quantum neural networks (QNNs); and (3) Fully Quantum Clustering, where a QNN is trained in a self-supervised manner leveraging a SwAV-based loss function to optimize the feature space for clustering directly. The proposed experimental framework systematically investigates the impact of quantum circuit depth and the number of learned prototypes, demonstrating that even shallow quantum circuits can effectively extract meaningful non-linear representations, significantly improving clustering performance.
We believe that leveraging real-time blockchain operational data is of particular interest in the context of the current rapid expansion of rollup networks in the Ethereum ecosystem. Given the compatible but also competing ground that rollups offer for applications, stream-based monitoring can be of use both to developers and to EVM networks governance. In this paper, we discuss this perspective and propose a basic monitoring pipeline.
Modern technological advancements such as 5G networks, smart devices, and the Internet of Things (IoT) have established a new digital landscape where dataflow security is a mandatory factor. In this context, blockchain technology stands out as a promising solution with the ability to provide a secure and invulnerable platform for systems. Blockchain is a distributed ledger, not only serving financial transactions but also securely and sustainably storing any type of data. Although blockchain originated from cryptocurrency (Bitcoin), its applicability has expanded into various fields such as data storage, product certification, healthcare, science, and education. In the field of education, blockchain is used to issue and store certificates, support degree management, assess learning outcomes, maintain academic records, and oversee training processes. This article analyzes the features and benefits of blockchain, presenting its current applications in education. At the same time, it discusses the benefits and challenges of implementing this technology in the field of education.
This paper offers a critical reassessment of Milton Friedman’s economic principles—monetarism, free-market competition, and limited government—in light of the rise of artificial intelligence (AI) and platform capitalism. Drawing on a structured qualitative literature review, the study explores how AI-driven economic structures challenge core assumptions embedded in Friedman’s theoretical framework. The analysis is organized around three key domains where traditional economic logic is being destabilized: (1) the erosion of competitive market dynamics through the rise of digital monopolies and algorithmic control; (2) the transformation of labor markets via automation, gig work, and AI-based management; and (3) the weakening of central bank authority amid the proliferation of decentralized finance and platform-based payment systems. Friedman envisioned markets as inherently self-correcting and efficient, but AI capitalism increasingly reveals the limitations of such views. Digital platforms leverage network effects, data accumulation, and algorithmic manipulation to entrench market power, creating structural barriers to entry that contradict the competitive ideal. Similarly, the gig economy, governed by opaque algorithms, distorts labor flexibility into labor precarity, contradicting Friedman’s belief in voluntary and efficient labor exchanges. On the monetary front, the expansion of private payment ecosystems and algorithmic lending challenges the foundational monetarist assumption that central banks can regulate the money supply effectively. While the analysis recognizes the continued relevance of Friedman’s normative commitment to individual autonomy and market-based coordination, it argues that his framework must be significantly revised to account for the institutional and technological dynamics of the digital age. The paper concludes by proposing a forward-looking governance agenda focused on antitrust reforms, algorithmic accountability, labor protections, and monetary innovation. In doing so, it contributes to the emerging literature that seeks to reconcile classical economic theories with the demands of a rapidly evolving AI-driven global economy.
Chitrita Devi, R. R. Shantha Spandana, G.V.T. Swapna, G Viswanath
This project provides a Cloud-Assisted Decentralized privacy-preserving Framework (CA-DPPF) that amalgamates cloud computing, blockchain generation, and IPFS to tackle the complexities of securely and efficaciously storing sensitive healthcare data. The framework utilizes ECDSA digital signatures and RSA encryption to assure strong person authentication and statistics safety, in accordance with present day developments in safeguarding healthcare information. IPFS is applied for scalable storage solutions, addressing the limitations of traditional centralized cloud services, as indicated in previous research. Blockchain era augments the system through supplying immutable document-preserving, mitigating the weaknesses of centralized systems. A rankings module is incorporated to guarantee the legitimacy of healthcare feedback, allowing people to assess doctors, with these checks securely documented on the blockchain to prevent manipulation. smart contracts, created in Solidity, enable secure transactions and govern user data at the Ethereum blockchain, making certain transparency and integrity in all interactions. The studies gives a spread that integrates the CHACHA20 encryption algorithm, strengthening computational efficiency and safety while complementing present encryption methods and improving usual system overall performance.
This study empirically examined the relationship between digital banking and business financing in Nigeria. The objective was to examine the relationship between various digital banking policies and the effect on business financing. Time series data were sourced from Central Bank of Nigeria statistical bulletin from 1992-2024. Multiple regression models were specifically estimated with the aid of econometrics view. The study modeled business financing as the function of Automated teller machine, Point of Sales and Electronic fund transfer. Ordinary least square methods of cointegration, unit root test and Vector error correction model was used. The study found that 40.1 percent movement in small business financing can be traced to variation in digital banking policy. The study found that point of sales have negative effect while electronic fund transfer and automated teller machine have positive effect on business financing in Nigeria. From the findings, the study concludes that digital banking does not significantly explained variation in small business financing in Nigeria. The study recommends that Central Bank of Nigeria should induce the variations of the bank liquidity policy. That the monetary authorities should ensure adequate quantity of money supply that positively affect private sector funding in Nigeria and the need to decentralize the operation of the banks in the urban cities. Policies should be formulated to extend the operation of the banks to the rural communities, this will enable the institutions to mobilize much deposit and increase credit to business organizations
The subject of the research is the development of the market of non - interchangeable tokens for works of art, which has become a new paradigm in the global art market of the 21st century. The purpose of the work is to determine the place and role of non - interchangeable tokens for works of art in the life of modern society. It is shown that the world is experiencing a rapid development of the market for non-fungible tokens (NFT), which includes digital works of art created from digital materials and not linked to real assets, and tokenized non–digital works of art linked to real tangible products of artistic creation, conscious human activity with aesthetic value. The speculative nature of the NFT resale market has been established, which initially caused a “gold rush”, and subsequently its adjustment due to the accelerated production and supply of art objects in NFT, which led to a drop in prices. It is revealed that the development of the NFT market requires solving the problem of legal qualification of digital assets and inheritance in Russia, whereas abroad they are considered as proof of ownership of a digital asset. Despite the development of the financial sector in Russia in the digitalization of financial services, the domestic NET market has just begun to form, in particular the platform Kefirium.ru provides an opportunity to buy and sell NFT for rubles. Conclusions are drawn about the need for further development of digitalization of financial services, in particular the market of non-interchangeable tokens for works of art in Russia, the formation of competition between domestic platforms for the purchase and sale of NFTs for rubles, as well as the legal qualification of digital assets and their inheritance in Russia.
Di tengah pesatnya pertumbuhan teknologi digital dan Artificial Intelligence (AI), pencurian karya digital menjadi ancaman nyata yang meresahkan para kreator. Hal ini tidak hanya mengancam hak ekonomi, tetapi juga merusak integritas ekosistem kreatif. Penelitian ini menawarkan solusi inovatif: integrasi Non-Fungible Token (NFT) dan smart contract untuk perlindungan hak cipta di era digital. Dengan mengumpulkan 150 data dari platform media sosial X menggunakan metode web crawling dan analisis labeling, ditemukan bahwa mayoritas publik mendukung upaya perlindungan karya digital. Studi ini kemudian mensimulasikan pembuatan NFT dan implementasi smart contract di blockchain Polygon melalui OpenSea, membuktikan bahwa pencatatan kepemilikan berbasis teknologi dapat menghadirkan sistem perlindungan yang otomatis, transparan, dan tahan manipulasi. Hasil ini tidak hanya membangun jalur baru perlindungan hak cipta yang lebih adil dan efisien, tetapi juga menegaskan pentingnya adaptasi hukum di dunia digital. Temuan ini membuka cakrawala baru bagaimana teknologi blockchain dapat menjadi benteng pertahanan kreator di tengah dinamika digital yang terus berubah.
Peter Haastrup, Anne Møller, Jette Kolding Kristensen, Linda Huibers
Denmark is known for its good population health, largely attributable to its effective healthcare system. This analysis of the Danish primary healthcare system with focus on general practice describes the system's overall structure, function, and financing. Further, it reviews some of the recent developments in organization and decentralization from secondary to primary care. Finally, we discuss some of the key challenges that primary care faces and potential areas for improvement to ensure a sustainable Danish healthcare system of high quality.
The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions.Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability.To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting.Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data.In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle.To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability.Empirical validation has been conducted using real-time data from the Borsa stanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models.Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency.The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.
The relevance of the article is due to the fact that there is currently an increasing need for an independent financial ecosystem that provides capital owners with full control over their money due to the fact that it is the development of a financial system based on modern technologies in power and financial relations. The subject of the research is the use of financial services based on the use of blockchain and a platform approach. The purpose of the work is to identify the problems, risks, advantages and disadvantages of decentralized finance (DeFi). The analysis of the current state of decentralized finance is carried out and the mechanisms of their functioning are investigated. It has been established that the DeFi ecosystem, which uses a multi-level structure and freely combines blocks and protocols, is implemented by decentralized autonomous organizations that ensure the interaction of participants and decision-making mechanisms. Potential applications of the DeFi ecosystem have been identified, covering the provision and receipt of loans, banking services, and profit optimization. The key problems of decentralized financing have been identified, including a high risk of user error; low productivity; the possibility of third-party interference; difficulties in using tokens with different capitalization levels; imperfect functioning of programs; insufficient cybersecurity; significant volatility. The advantages and development trends of centralized financing are highlighted: fast access and openness; autonomy; relatively high profitability; savings on resources and time. It is concluded that decentralized financing has both advantages and disadvantages.
Retroactive Public Goods Funding (RetroPGF) rewards blockchain projects based on proven impact rather than future promises. This paper reviews voting mechanisms for Optimism's RetroPGF, where "badgeholders" allocate rewards to valuable projects. We explore Optimism's previous schemes for RetroPGF voting, including quadratic, mean, and median voting. We present a proof-based formal analysis for vulnerabilities in these voting schemes, empirically validate these vulnerabilities using voting simulations, and offer assessments and practical recommendations for future iterations of Optimism's system based on our findings.
Nasim Paykari, Ali Alfatemi, Damian M. Lyons, Mohamed Rahouti
This work explores a novel integration of blockchain methodologies with Wide Area Visual Navigation (WAVN) to address challenges in visual navigation for a heterogeneous team of mobile robots deployed for unstructured applications in agriculture, forestry, etc. Focusing on overcoming challenges such as GPS independence, environmental changes, and computational limitations, the study introduces the Proof of Stake (PoS) mechanism, commonly used in blockchain systems, into the WAVN framework \cite{Lyons_2022}. This integration aims to enhance the cooperative navigation capabilities of robotic teams by prioritizing robot contributions based on their navigation reliability. The methodology involves a stake weight function, consensus score with PoS, and a navigability function, addressing the computational complexities of robotic cooperation and data validation. This innovative approach promises to optimize robotic teamwork by leveraging blockchain principles, offering insights into the scalability, efficiency, and overall system performance. The project anticipates significant advancements in autonomous navigation and the broader application of blockchain technology beyond its traditional financial context.
Voting is a cornerstone of democracy, allowing citizens to express their will and make collective decisions. With advancing technology, online voting is gaining popularity as it enables voting from anywhere with Internet access, eliminating the need for printed ballots or polling stations. However, despite its benefits, online voting carries significant risks. A single vulnerability could be exploited to manipulate elections on a large scale. Centralized systems can be secure but may lack transparency and confidentiality, especially if those in power manipulate them. Blockchain-based voting offers a transparent, tamper-resistant alternative with end-to-end verifiability and strong security. Adding cryptographic layers can also ensure voter confidentiality.
The integration of unmanned aerial vehicles (UAVs) into smart agriculture has enabled real-time monitoring, data collection, and automated farming operations. However, the high mobility, decentralized nature, and low-power communication of UAVs pose significant security challenges, particularly in ensuring transaction integrity and trust. This paper presents a quantum-resilient blockchain framework designed to secure data and resource transactions in UAV-assisted smart agriculture networks. The proposed solution incorporates post-quantum cryptographic primitives-specifically lattice-based digital signatures and key encapsulation mechanisms to achieve tamper-proof, low-latency consensus without relying on traditional computationally intensive proof-of-work schemes. A lightweight consensus protocol tailored for UAV communication constraints is developed, and transaction validation is handled through a trust-ranked, multi-layer ledger maintained by edge nodes. Experimental results from simulations using NS-3 and custom blockchain testbeds show that the framework outperforms existing schemes in terms of transaction throughput, energy efficiency, and resistance to quantum attacks. The proposed system provides a scalable, secure, and sustainable solution for precision agriculture, enabling trusted automation and resilient data sharing in post-quantum eras.
Vehicular Fog Computing (VFC) is a promising paradigm to meet the low-latency and high-bandwidth demands of Intelligent Transportation Systems (ITS). However, dynamic vehicle mobility and diverse trust boundaries introduce critical security challenges. This paper presents a novel Zero-Trust Mobility-Aware Authentication Framework (ZTMAF) for secure communication in VFC networks. The framework employs context-aware authentication with lightweight cryptographic primitives, a decentralized trust evaluation system, and fog node-assisted session validation to combat spoofing, replay, and impersonation attacks. Simulation results on NS-3 and SUMO demonstrate improved authentication latency, reduced computational overhead, and better scalability compared to traditional PKI and blockchain-based models. Our findings suggest that ZTMAF is effective for secure, real-time V2X interactions under adversarial and mobility-variant scenarios.
Francesco Salzano, Cosmo Kevin Antenucci, Simone Scalabrino, Giovanni Rosa · 6 authors
Abstract The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances, manually labeled at the line level with vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the efficacy of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78% found vulnerabilities, taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and by conducting the largest empirical evaluation of tools to date.
Payment channel networks are an approach to improve the scalability of blockchain-based cryptocurrencies. The Lightning Network is a payment channel network built for Bitcoin that is already used in practice. Because the Lightning Network is used for transfer of financial value, its security in the presence of adversarial participants should be verified. The Lightning protocol's complexity makes it hard to assess whether the protocol is secure. To enable computer-aided security verification of Lightning, we formalize the protocol in TLA+ and formally specify the security property that honest users are guaranteed to retrieve their correct balance. While model checking provides a fully automated verification of the security property, the state space of the protocol's specification is so large that model checking becomes unfeasible. We make model checking the Lightning Network possible using two refinement steps that we verify using proofs. In a first step, we prove that the model of time used in the protocol can be abstracted using ideas from the research of timed automata. In a second step, we prove that it suffices to model check the protocol for single payment channels and the protocol for multi-hop payments separately. These refinements reduce the state space sufficiently to allow for model checking Lightning with models with payments over up to four hops and two concurrent payments. These results indicate that the current specification of Lightning is secure.
Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure. In this work, we propose a novel Federated Learning-Enhanced Blockchain Framework (FL-BCID) for privacy-preserving intrusion detection tailored for IIoT environments. Our architecture combines federated learning (FL) to ensure decentralized model training with blockchain technology to guarantee data integrity, trust, and tamper resistance across IIoT nodes. We design a lightweight intrusion detection model collaboratively trained using FL across edge devices without exposing sensitive data. A smart contract-enabled blockchain system records model updates and anomaly scores to establish accountability. Experimental evaluations using the ToN-IoT and N-BaIoT datasets demonstrate the superior performance of our framework, achieving 97.3% accuracy while reducing communication overhead by 41% compared to baseline centralized methods. Our approach ensures privacy, scalability, and robustness-critical for secure industrial operations. The proposed FL-BCID system provides a promising solution for enhancing trust and privacy in modern IIoT security architectures.
With the rapid development of blockchain technology, various blockchain systems are exhibiting vitality and potential. As a representative of Blockchain 3.0, the EOS blockchain has been regarded as a strong competitor to Ethereum. Nevertheless, compared with Bitcoin and Ethereum, academic research and in-depth analyses of EOS remain scarce. To address this gap, this study conducts a comprehensive investigation of the EOS blockchain from five key dimensions: system architecture, decentralization, performance, smart contracts, and behavioral security. The architectural analysis focuses on six core components of the EOS system, detailing their functionalities and operational workflows. The decentralization and performance evaluations, based on data from the XBlock data-sharing platform, reveal several critical issues: low account activity, limited participation in the supernode election process, minimal variation in the set of block producers, and a substantial gap between actual throughput and the claimed million-level performance. Five types of contract vulnerabilities are identified in the smart contract dimension, and four mainstream vulnerability detection platforms are introduced and comparatively analyzed. In terms of behavioral security, four real-world attacks targeting the structural characteristics of EOS are summarized. This study contributes to the ongoing development of the EOS blockchain and provides valuable insights for enhancing the security and regulatory mechanisms of blockchain ecosystems.
Financial disruptions, such as extended trade credit periods and reduced down payments, pose significant challenges to effective working capital management in supply chains. While the benefits of blockchain and digital twin technologies have been studied independently in supply chain finance, their combined potential to optimise working capital performance during disruptions remains underexplored. Our study addresses this research gap by proposing an integrated blockchain and digital twin framework to enhance financial resilience in disrupted supply chains. Blockchain facilitates secure, decentralised data sharing, providing visibility into product, order, and cash flows across supply chain stages. The digital twin complements blockchain by offering predictive capabilities and enabling dynamic adjustments to working capital policies in response to disruptions. Within this framework, discrete-event simulation assesses the impact of financial disruptions on working capital performance, while machine learning models generate decision rules for adaptive working capital management. The study highlights the critical role of inventory adjustments in mitigating financial disruptions and reducing working capital variability relative to demand fluctuations. This research provides actionable insights for supply chain managers seeking to improve working capital stability amid disruptions and offers a data-driven approach to financial resilience in supply chains.