The advent of blockchain technology has created a paradigm shift in the way digital data can be securely, transparently and decentralized managed, a paradigm that could potentially replace the longstanding centralized digital information systems. This is a full journal-grade review of blockchain as a tool to ensure trustless, immutability and decentralized data governance in a variety of important application fields. The study, which is based on a systematic review of 20 peer-reviewed publications from 2023 to 2025, explores the essential structural elements of blockchain systems: Distributed ledger structures, cryptographic hash functions, Merkle tree integrity verification, consensus mechanisms, and smart contracts, and how they all contribute to removing single points of failure and institutional trust dependencies. There is a comparative study of the various public, private and consortium blockchain types, as well as the evaluation of the various consensus algorithms, such as Proof of Work (PoW), Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The results show that data management systems based on blockchain technology always have superior data integrity, access auditability, censorship resistance, and user data sovereignty properties compared to centralized systems, and come with trade-offs in scalability, energy efficiency, and compliance with regulations. Evidence collected for the application has come from health care organizations' record management, supply chain traceability, decentralized identity systems, Internet of Things (IoT) data integrity, an energy company data management system, and cybersecurity threat intelligence, among other contexts. Key challenges and emerging technologies, such as quantum computing systems, post quantum cryptographic standards, layer-two rollups, sharding and zero-knowledge proofs, are explored in tandem with the blockchain trilemma, GDPR compliance issues and cross-chain interoperability. The study finds that blockchain-based data management is moving from the experimental stage to becoming a core component to the digital economy's infrastructure.
While decentralized fintech platforms (DFPs) are a viable alternative to traditional financial systems, their widespread adoption is hindered by cybersecurity concerns. This study investigates the factors shaping user intentions to adopt DFPs amid these perceived cyber risks. Situated within the global challenge of financing the Sustainable Development Goals (SDGs), this research explores DFP adoption as a potential mechanism for enhancing financial inclusion (SDG 10), fostering innovation in financial infrastructure (SDG 9), and promoting inclusive economic growth (SDG 8). Using a hybrid model that merges the technology acceptance model with trust theory, we surveyed 554 business and engineering students across five Indian metropolitan hubs a demographic representing a tech-forward segment of Gen Z poised to be early adopters. Structural equation modelling reveals that while fintech literacy and risk tolerance are significant preconditions, adoption intention is most powerfully predicted by social influence (β = 0.271, p < 0.001) and perceived security in the underlying technology (β = 0.186, p < 0.000). This suggests that for these vanguard users, the decision to adopt is driven more by social validation and technological faith than by individual technical competency. Furthermore, trust moderates the relationship between fintech literacy, perceived security and risk tolerance on the intent to adopt. The paper contributes a comprehensive framework that clarifies users’ adoption motives, aiding researchers who study DFP user behavior in developing nations. The findings provide a critical roadmap for policymakers, developers, and international development agencies aiming to harness digital finance for the 2030 Agenda, demonstrating that building trusted, community-driven ecosystems is paramount to realizing the developmental potential of decentralized technologies.
Proof of Work (PoW) and Proof of Stake (PoS) remain the dominant consensus mechanisms for public blockchains, yet both suffer from well-documented limitations: PoW incurs prohibitive energy costs and scales poorly with network size, while PoS tends to concentrate validator influence among large stake holders, reintroducing a form of economic centralization.This paper proposes Proof of Effort (PoE), a novel consensus mechanism in which validator eligibility is determined exclusively by verifiable, continuous protocol participation rather than by computational expenditure or financial stake.PoE integrates four lightweight security primitives -node-identity binding via asymmetric key pairs, sequence-number-based replay prevention, digital-signature message authentication, and threshold-based malicious-node detection -chosen specifically to impose negligible per-node overhead while maintaining Byzantine resilience.We formalize the effort accumulation model mathematically, provide a detailed pseudocode specification of the protocol, and evaluate the mechanism through an agent-based simulation implemented in NetLogo 6.4 with 500 heterogeneous agents (350 honest, 150 adversarial).Simulation results, presented with full graphical and tabular support, demonstrate that PoE achieves an average throughput of 900 tx/s (vs. 25 tx/s for PoS and 7 tx/s for PoW), reduces average consensus latency to 56 ms (vs.400 ms and 1,200 ms), and correctly identifies 92-97% of malicious agents while accepting fewer than 0.2% of forged or replayed messages.The effort gap between honest and adversarial nodes remained below 0.21%, confirming protocol fairness under adversarial load, and the validator-selection Gini coefficient of 0.18 represents a 71% improvement in participation equality over PoS.These results position PoE as a compelling candidate for permissioned and consortium blockchain deployments where energy efficiency, participation fairness, and lightweight security are primary requirements.
Ethereum, one of the largest blockchain platforms, manages millions of dollars in digital assets daily and depends on consistent community standards and cohesive implementations to maintain network consensus. This study introduces a novel methodology for identifying and measuring temporal inconsistencies in opcode mappings across Ethereum clients and the Solidity compiler. Using this methodology, we uncover significant and previously undocumented discrepancies both among different components of Ethereum’s implementation and between theoretical specifications and practical execution behavior. We further demonstrate the potential consequences of these inconsistencies by illustrating their impact on the detection of vulnerabilities in Solidity smart contracts. Such discrepancies can substantially undermine the accuracy and reliability of vulnerability analysis, highlighting the critical need for strict consistency in Ethereum’s operational semantics. Ensuring this consistency is essential for preserving transaction security and the overall robustness of the platform.
Decentralised Autonomous Organisations (DAOs) raise fundamental questions for private law. While scholarly and regulatory attention has primarily focused on the legal status and governance of DAOs, comparatively little consideration has been given to how disputes involving these organisations should be resolved. This article examines the suitability of dispute resolution mechanisms for DAO-related disputes. Analysing the principal categories of disputes that have emerged in practice, the article critically evaluates blockchain-based dispute resolution mechanisms alongside traditional arbitration, in light of due process, enforceability, party autonomy, and the unique features of decentralised governance. It argues that neither purely code-based dispute resolution nor conventional litigation provides a satisfactory response to the complexity of DAO disputes. It concludes that arbitration, appropriately adapted to the technological and organisational realities of DAOs, offers the most promising framework for balancing decentralisation, legal certainty, and procedural fairness within the evolving Web3 ecosystem.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privacy (transparency) of the smart contracts managed by the DAO and its stakeholders. In this work, we (i) explicate the trust and transparency trade-offs of the design choices in implementing a DAO and (ii) highlight how poor choices introduce critical vulnerabilities, using real-world examples as case studies. To this end, we analyze $48$ public, actively used Ethereum-based DAOs that control a vast capital. We classify the design choices into a handful of key dimensions that succinctly capture how a DAO's stakeholders initiate a protocol change, vote on it, and, based on the voting outcome, execute that change. Our analyses crucially uncover a new class of attacks, which we call governance attacks, that directly exploit the fundamental design of a DAO's governance mechanisms, even if we assume bug-free implementations.
Jemima Owusu-Tweneboah, Amani Altarawneh, Deepti Gupta, Maria Luisa Figueroa
Cybersecurity and privacy frameworks such as NIST SP~800--53, ISO/IEC~27001, GDPR, and HIPAA are widely used to guide organizational security posture and regulatory compliance. In practice, however, framework adoption is often assessed through point-in-time audits, self-attestations, and fragmented evidence reviews, providing limited assurance that controls are consistently implemented, independently validated, and sustained over time, particularly in environments that rely on third-party vendors. These limitations are amplified in multi-vendor ecosystems, such as healthcare remote patient monitoring (RPM), where compliance obligations span organizational boundaries and assessments are conducted by multiple independent assessors. This paper investigates how permissioned blockchain systems can support framework implementation maturity measurement rather than static compliance verification. We propose a blockchain-based Third-Party Risk Assessment (TPRA) framework that operationalizes assessment workflows, enforces multi-party governance, and preserves longitudinal assessment state using programmable smart contracts. Building on this framework, we introduce a set of evaluation metrics and a qualitative maturity model designed to assess whether compliance controls are verifiably implemented, governed, and sustained across repeated assessment cycles.
Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.
The long and distinguished history of the Wikimedia ecosystem has provided valuable insights and lessons learned regarding the dispute over control of knowledge on the web. This activity aims to contribute to the point: “Pilot experiments in decentralized decision-making” of the Wikimedia Foundation's Annual Plan, bringing to the public community discussion table practical and real use cases in which, beyond the purely speculative and financial aspects of the blockchain ecosystem, Web3 can offer highly efficient tools to promote and guarantee knowledge equity. In this space, the functioning of some Web3 tools for decentralized governance will be presented in a practical way, establishing parallels with existing mechanisms in the Wikimedia ecosystem, such as MetaWiki SecurePoll, RFCs, Community Voting, and the Board of Trustees.
We solve Bellman's lost-in-a-forest problem for the golden gnomon $G$, the isosceles triangle with equal sides $1$ and apex angle $108^\circ$: the shortest curve guaranteed to reach the boundary of $G$ from an unknown starting position and heading is a symmetric seven-piece path of segments, circular shoulders, and tangents, of exactly determined length $C=1.282676025459\ldots$. To our knowledge, this is the first proved exact optimum for an isosceles triangle whose base angle is below $45^\circ$. The curve's parameters come from one isolated quartic root, and $C$ is transcendental. Equivalently, $C^{-1}G$ is the smallest homothetic golden-gnomon cover of all unit arcs. The proof introduces a balanced support calibration: one weighted family of escape inequalities, built on the linear relation among the triangle's three normals, exactly saturated by the candidate, through eighteen exact support windows, and confronting every shorter competitor at once. Aggregation along the normal fan compresses the calibration to a finite zero-sum family of supported vectors; summation by parts then bounds its total by path length whenever the running suffix balance, the ledger, stays in the unit disk. A local two-gap surgery and cyclic bitonicity force a shortest hypothetical counterexample into exactly the temporal order the ledger tolerates. Lean 4 verifies the two finite algebraic certificate families and the reusable discrete ledger identities and bounds.
The Financial Technology (FinTech) ecosystem has become a disruptive one which changed the face of the financial services industry with digital technology, innovative business models and new regulatory framework. The development, distribution and use of financial products and services have been revolutionized by technologies like artificial intelligence, blockchain, cloud, big data analytics, Internet of Things and open banking. This review paper aims to integrate and consolidate the available literature to gain an overview of the development of FinTech from Finance 1.0 to Finance 4.0, and their technological innovations as the backbone of the modern financial systems. It also explores some of the key FinTech business models like digital payments, digital lending, WealthTech, InsurTech and embedded finance, and the essential role of relevant government policies, digital public infrastructure and governance for responsible FinTech innovation. The paper also identifies relevant challenges in the fields of cybersecurity, data privacy, ethics in artificial intelligence, regulatory complexity and digital inclusion that remain to significantly impact the sustainable development of the FinTech ecosystem. Last but not least, new research opportunities are identified in the field of generative artificial intelligence, decentralized finance, green FinTech, and digital financial governance to be pursued by academia in the future. The multidisciplinary perspective employed in this review gives a comprehensive picture of the current developments in FinTech and can help researchers, practitioners and policymakers to understand the opportunities and risks associated with digital financial transformation.
The global healthcare supply chain is experiencing increasing challenges with respect to maintaining the security of medications, adequate storage of medications, and identifying potential issues with medications that may pose a risk to patient safety. This chapter introduces a prototype called the 'Smarter, Safe Healthcare Supply Chain.' The system brings together fast telemetry (simulating 4G and 5G), edge computing, blockchain smart contracts, and explainable anomaly detection to help stop compromised medications from reaching patients. Included in the prototype's design are an IoT temperature and location data sensing simulator; an edge service that checks the signature of a device and executes "explainable" checks; and two smart contracts associated with device tracking and alerting. Tools like latency simulation, on-chain device tracking, device allowance (i.e., allowing only those devices that have been verified via smart contracts to access the network), and audit logs allow for the prototype to demonstrate how new technologies can enhance, accelerate, and streamline operations and build trust throughout the supply chain. Some of the key results of the prototype include faster-than-anticipated response times under 5G simulated conditions, successful verification of devices (both at the edge and enterprise-level) via smart contract(s), and accurate alerting developed based on a predefined set of rules. The framework upon which the prototype is built follows the principles of Zero Trust (e.g., NIST SP 800-207A, GSMA 5G IoT Guidelines, and use case-specific Healthcare Compliance Controls). Limitations exist within the prototype (such as being a single-node blockchain and the use of rule-based alerting versus leveraging full machine learning capabilities); however, it presents a viable operational model for practical application within a regulated supply chain (e.g., pharmaceuticals). Future work will include multi-party blockchain networks, evolving AI algorithms, and demonstrating full integration of the prototype into existing regulatory workflow(s).
Abstract Coral reefs across the Coral Triangle are experiencing unprecedented thermal stress, and the window for effective local mitigation of bleaching events is often measured in days, not months. Yet the standard reef-monitoring pipeline — collecting benthic imagery via divers or remotely operated vehicles (ROVs) and manually annotating it for signs of bleaching — routinely takes weeks to complete, a delay we term the manual-annotation bottleneck. This paper presents CoralEye, a decentralized, edge-optimized computer vision framework that diagnoses coral health in real time and is designed to eventually run entirely offline on consumer-grade hardware. CoralEye uses an optimized MobileNetV2 architecture, quantized to INT8 for edge deployment, trained on a curated subset of the Benthic Habitat Data (BHD) dataset to classify reef imagery into a three-class triage system — Healthy, Bleached, and Dead. On a held-out test set of 636 images, the framework achieved an overall accuracy of 97% (weighted-average precision 0.98, recall 0.97, F1-score 0.97) while processing underwater imagery at an average latency of 177 milliseconds per frame (∼5.6 FPS), benchmarked in a Google Colab CPU-only cloud runtime as a proxy for offline, GPU-free edge hardware. Relative to established manual annotation workflows such as Coral Point Count (CPCe), this proxy benchmark suggests a reduction in per-image processing time of over 99%, though this comparison has not yet been validated on a physical field device. We argue that this combination of accuracy and CPU-only efficiency is a promising step toward a shift in marine conservation technology from post-mortem documentation toward real-time, actionable diagnostics — particularly for under-resourced reef managers in bandwidth-constrained coastal communities, pending further validation on physical field hardware.
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
Chibueze Ubochi Nwamouh, Okereke Aru-Eze, Wilson C. Ahiara, Isaac Adewale Samuel
One potential remedy for the problems with online voting methods is blockchain technology. Online voting has several benefits, such lower costs and higher voter turnout, but its broad adoption has been hampered by security and manipulation issues. With features like non-repudiation and end-to-end verification, blockchain&s;s decentralized and secure architecture provides an alternative to electronic voting. In addition to examining the present status of research and development in this area, this article gives an overview of blockchain-based electronic voting systems. The objective is to pinpoint current issues and predict upcoming developments in blockchain-based voting. Paper clarifies the core features of blockchain technology that are pertinent to voting and highlights the conceptual framework of blockchain-based electronic voting systems. According to the report, blockchain technologies might help with some of the problems that conventional election systems have. Transaction speed and privacy protection, however, are two significant obstacles that stand out. Blockchain-based electronic voting systems must be implemented sustainably, which requires resolving scalability issues with transaction speed and guaranteeing the security of distant participation. Current frameworks must be improved in order to properly utilize blockchain technology in voting systems. This entails strengthening privacy safeguards and figuring out ways to speed up transactions. Blockchain-based electronic voting systems have the potential to be a safe and effective alternative for next elections if these issues are resolved.
Blockchain technology provides a decentralized and secure data-management system. However, PoW consensus remains vulnerable to the risk of 51% attacks, where a single miner or pool controls over half of the network and can manipulate blocks and execute double-spending. This paper proposes a defense mechanism called Consensus-Level Restriction (CLR) for Ethereum Classic. It limits the number of sequential blocks from a single miner to reduce the feasibility of 51% attacks. We evaluated the effectiveness of CLR by using BlockSim simulator. Results show that CLR reduces the success rate of 51% attacks while maintaining a decentralized structure. This work strengthens blockchain security without compromising PoW principles.
When is honest Bitcoin mining rational? This question is central to the incentive design of proof-of-work blockchains. Sapirshtein et al. computationally derived near-tight lower and upper bounds on the incentive-compatibility threshold using a Markov Decision Process. Kiayias et al.'s Blockchain Mining Games instead derived theoretical lower and upper bounds. However, this theoretical approach has two limitations: its model restricts miners to a narrow action space and assumes idealized tie behavior, and its lower and upper bounds are far from tight. We resolve both limitations. We develop a more realistic model with a broader miner action space and asymmetric tie-breaking parameters $γ^-$ and $γ^+$. We then propose an algorithm that computes lower and upper bounds on the incentive-compatibility threshold with a maximum error of $9.98006\times10^{-4}$.
We study the impact that two miners equipped with quantum computers purpose-built for quantum Bitcoin mining will have on the 51% attack threshold of the Bitcoin network, given that the miners are playing a competitive game against each other to be the first to mine a block. We extend an existing game-theoretic framework for Bitcoin mining and compute the resultant payoff matrices. From these payoff matrices, we determine optimal quantum mining strategies for two non-colluding and aggressive quantum miners with multiple opportunities at finding a valid block in an otherwise classical Bitcoin network. We show that these optimal quantum mining strategies have a negligible effect on the 51% attack threshold. The novelty of our work is the inclusion of the Aggressive Quantum Mining Strategy and the realistic approach of allowing the quantum miners to restart their search if their measurements do not yield a valid block when determining the optimal quantum mining strategies. Our result is important for evaluating quantum-mining threats on cryptocurrencies based on Proof-of-Work, e.g. Bitcoin
Language-model efficiency is often discussed through concepts that are related but not equivalent: token count, embedding dimensionality, parameter count, key-value cache size, and retrieved-context length. This article presents a structured critical review, supported by document-level traceability, of matrix and vector methods used to reduce storage and inference costs in language models and retrieval-augmented generation (RAG) systems. The analysis shows that matrix factorization, adaptive embeddings, and Matryoshka representations compress parameters or vectors but do not automatically decrease the number of input tokens. Direct token reduction requires sequence-level interventions, including appropriate tokenization, selection, prompt compression, gist tokens, and retrieved-context control. KV-cache compression reduces memory and latency without necessarily changing the tokens submitted to the model. We propose a mathematical framework that separates V (vocabulary size), d (embedding dimension), n (sequence length), k (number of retrieved documents or passages), and nKV (cache positions retained).
The widespread problem of cyberbullying in today’s digital environment is made worse by the quick spread of IoT devices and the difficulties in organizing and protecting digital evidence. Traditional forensic methods are often inadequate due to their inability to provide a tamper-proof chain-of-custody and their limited scalability under high data loads. To overcome these constraints, our work makes use of innovative technologies such as a permissioned blockchain (Hyperledger Fabric), powerful encryption methods (AES-256 and RSA), and decentralized off-chain storage (IPFS). We propose an integrated, blockchain-driven forensic evidence collection framework that ensures secure, real-time evidence acquisition from diverse IoT devices, automated validation via smart contracts, and efficient, Role-Based Access Control for evidence retrieval. A hybrid consensus mechanism, combining elements of PBFT and Proof-of-Stake, enhances the system’s security and scalability while reducing processing latency and energy consumption. Experimental results demonstrate that our approach achieves high throughput and low latency, making it a strong and reliable solution for forensic investigations in cyberbullying cases. Within our experimental scope, the framework strengthens the integrity and authenticity of digital evidence and addresses key regulatory considerations, though real-world validation remains future work.
The governance of new technology projects in remote and developing economies is frequently undermined by severe information asymmetry and fragmented telecommunications infrastructure. While theoretical public policy advocates for the deployment of distributed ledger technologies to enhance institutional design and regulatory transparency, evaluating the economic impact of these systems relies heavily on static, retrospective datasets. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless pipeline for telecommunications governance and blockchain integration. By deploying asynchronous Python middleware integrated with simulated smart contracts, the proposed system programmatically ingests high-frequency network telemetry and cross-border telecommunications data. The system translates these inputs into a dynamic Institutional Transparency Index, instantly identifying regulatory bottlenecks and pricing friction across public-private partnerships. Preliminary architectural evaluations demonstrate that decoupling the governance tracking from legacy, centralized state databases significantly reduces information asymmetry, providing policy researchers with a deterministic, highly scalable tool for modeling the economics of distributed ledgers in developing contexts.