Blockchain technology has evolved from its origin as the foundation of cryptocurrencies into a versatile, decentralized framework for secure data management. Its core features include decentralization, immutability, transparency, and cryptographic security which enable trustworthy interactions without centralized authority. This review presents a comprehensive examination of blockchain fundamentals, including architecture, consensus mechanisms, and smart contracts, followed by applications across finance, supply chain, healthcare, IoT, and government systems are highlighted.
Theo Tzanidis, Veronica Scuotto, Lea Iaia, Marco Pironti
Digital entrepreneurship is increasingly recognised as an iconic enterprise phenomenon of the 21st century. It has generated new forms of entrepreneurship like Artificially Intelligent Entrepreneurs who are supported by the use of DARQ+ technologies—Distributed Ledger Technology, Artificial Intelligence (AI), Extended Reality (XR), and Quantum Computing. In turn, it has shaped new virtual realities included metaverse spaces to exploit new opportunities and modelling current business. This has advanced educational programmes to satisfy the new demand. In this scenario, this chapter explores the literature of digital entrepreneurship, offering emerging studies and recommendations for future research, querying how the future digital entrepreneur will be.
Bitcoin’s extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov Model–Gated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.
Arthur Ramos, Anjolina Grisi de Oliveira, Ruy de Queiroz, Tiago M. L. de Veras
We present Metatheory, a comprehensive library for programming language foundations in Lean 4. The library features a modular framework for proving confluence of abstract rewriting systems using three classical proof techniques: the diamond property, Newmans lemma, and the Hindley-Rosen lemma. These are instantiated across six case studies including untyped lambda calculus, combinatory logic, term rewriting, simply typed lambda calculus, and STLC with products and sums. All theorems are fully mechanized with zero axioms or sorry statements. We provide complete proofs of de Bruijn substitution infrastructure and demonstrate strong normalization via logical relations. To our knowledge, this is the first comprehensive confluence and normalization framework for Lean 4.
Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies.
Artificial intelligence increasingly governs access to credit, employment, and identity verification, raising questions of rights protection when deployed across borders. This paper develops a computable framework for Fundamental Rights Impact Assessment (FRIA) that transforms the legal principles of necessity and proportionality into quantifiable metrics. By embedding these standards into algorithmic pipelines, the framework enables verifiable auditing of high-risk AI systems. Simulations were conducted in two domains, credit scoring and biometric authentication, using synthetic datasets modeled on European and non-European jurisdictions. The necessity audits reduced the average input set by 24.6 ± 2.3 variables while sustaining predictive accuracy, while proportionality assessments exposed heavy reliance on sensitive features in 39%* of credit scoring models and significant subgroup disparities in biometric authentication. Distributed verification protocols preserved results on blockchain ledgers, ensuring transparency and cross-border accountability. The findings demonstrate that computable FRIAs can operationalize fundamental rights obligations, producing results that can be inspected by regulators and reviewed in courts. The study concludes that computable methods offer a practical bridge between jurisprudential principles and algorithmic implementation, though persistent divergences in cross-border proportionality standards remain a major challenge for harmonized enforcement.
In the Aether Physics Model (APM), each elementary particle is a distributed-charge excitation of an Aether unit with two electrostatic spheres and four magnetic loxodromes in five dimensions. The Quantum Measurement Units (QMU) system expresses all ledgers in terms of the base atoms $m_e$, $\lambda_C$, $F_q$, $e^2$, and ${e_\mathrm{emax}}^2$, with the Aether unit $A_u$ and the curl unit $\mathrm{curl}$ satisfying the rotational identity\[A_u \cdot \mathrm{curl} = {F_q}^2 {\lambda_C}^2.\] This article develops a complete QMU ledger for the superconducting electron pair. A superconducting pair is modeled as two electrons mutually occupying each other's positive electrostatic spheres, with their magnetic loxodromes polarly aligned so that south poles are adjacent and the chronovibrational Singularity lies between them. This configuration traps torsion internally, strongly suppresses the external curl, and leaves the Aether rotational identity intact. The QMU enrg unit and temp unit are defined by the electron rest-enrg and the Aether rotational ledger,\[\mathrm{enrg} = m_e {\lambda_C}^2 {F_q}^2,\qquad\mathrm{temp} = {F_q}^2 {\lambda_C}^2,\]so that all superconducting observables can be written without reference to SI/MKS units. For the superconducting pair we obtain\[m_{\mathrm{pair}} \approx 2 m_e,\qquadQ_{\mathrm{pair}} = 2 e^2,\]and introduce the magnetic cancellation parameter $\eta_{\mathrm{pair}}$ via\[\mathrm{curl}_{\mathrm{ext}}^{(\mathrm{pair})}= (1 - \eta_{\mathrm{pair}})\,\mathrm{curl}_{\mathrm{int}},\]with $\mathrm{curl}_{\mathrm{int}} \approx \mathrm{curl}$ for the combined Aether unit. This leads to an effective external magnetic charge\[e_{\mathrm{eff}}^2 = 2 (1 - \eta_{\mathrm{pair}})\, e_{\mathrm{emax}}^2,\]and a pair flux unit\[\mathrm{mflx}_{\mathrm{pair}}= \frac{m_{\mathrm{pair}} \lambda_C^2 F_q}{e_{\mathrm{eff}}^2}= \frac{\mathrm{mflx}}{1 - \eta_{\mathrm{pair}}},\]so that the pair becomes magnetically ``invisible'' as $\eta_{\mathrm{pair}} \to 1$. The pair binding enrg $E_{\mathrm{bind}}$ is written in units of $\mathrm{enrg}$,\[E_{\mathrm{bind}} = \beta_{\mathrm{pair}}\,\mathrm{enrg},\qquad0 < \beta_{\mathrm{pair}} \ll 1,\]and the superconducting transition is expressed as a ledger equality between the binding ledger and a chronovibrational thermal ledger,\[E_{\mathrm{th}}(\theta_c) = f_{\mathrm{th}}(\theta_c)\,\mathrm{enrg}\approx E_{\mathrm{bind}},\qquad\theta_c = T_c / \mathrm{temp},\]so that $f_{\mathrm{th}}(\theta_c) \approx \beta_{\mathrm{pair}}$ defines the critical temp in pure QMU. Material dependence is encoded in three dimensionless parameters:\[\eta_{\mathrm{pair}},\qquad\beta_{\mathrm{pair}},\qquad\Xi_{\mathrm{Aether}},\]where $\Xi_{\mathrm{Aether}}$ is an Aether–lattice coupling index that measures how well the lattice geometry supports positive-sphere mutual occupation, south–south loxodrome alignment, and chronovibrational phase locking along conduction paths. Using penetration-depth and gap-ratio data from conventional superconductivity experiments, the paper constructs two complementary QMU maps: 1. A superconductivity engineering map in terms of a composite pair exponent $\alpha_{\mathrm{pair}}(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}})$, showing that A15 compounds, cuprates, and hydrides occupy distinct but ordered regions of the $(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}})$ ledger space. 2. A superconductivity parameters plot in the plane\[\left(\sigma_{\mathrm{pair}}^{(T)},\, \sigma_{\mathrm{pair}}^{(\mathrm{iso})}\right),\]where $\sigma_{\mathrm{pair}}^{(T)}$ is a critical-temp–scaled pairing parameter and $\sigma_{\mathrm{pair}}^{(\mathrm{iso})}$ is an isotope-effect parameter. All known materials fall close to a universal straight line\[\sigma_{\mathrm{pair}}^{(\mathrm{iso})}\approx \sigma_A - \sigma_{\mathrm{pair}}^{(T)},\]with $\sigma_A$ a dimensionless constant. In the QMU interpretation this line is the shared superconducting pair ledger relating the projections of $\eta_{\mathrm{pair}}$, $\beta_{\mathrm{pair}}$, and $\Xi_{\mathrm{Aether}}$. The article closes with an experimental outlook formulated entirely in QMU, including chronovibrational sensitivity tests, Aether–lattice design heuristics, and a program for extracting $(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}},\Xi_{\mathrm{Aether}})$ from future superconductivity data sets. All results are presented in QMU-only form, with SI/MKS appearing only as a secondary cross-check in the appendix.
Purpose The purpose of this paper is to analyse privacy and national surveillance laws in Australia, including but not limited to the federal Privacy Act 1988 and the federal Anti-Money Laundering and Counter-Terrorism Financing Act 2006 (AML/CTF), to determine whether a tension exists between these two statutes within the context of the deployment of central bank digital currency (CBDC) in Australia. Design/methodology/approach The paper adopts doctrinal and normative approaches for analysis of the relevant legislations. Furthermore, the paper adopts a functionalist theoretical perspective to interrogate the interrelationship between regulation and society. Findings The paper suggests that the relevant legislations contain undefined terms, such as “reasonable grounds”, which may give the appearance of a balanced approach to the interactions between privacy and national surveillance laws within the context of deployment of CBDCs. The paper argues that lack of definition of these terms, however, renders the terms ineffective, leading to a potential regulatory overreach. The paper recommends administrators need internal policies and procedures that would give clear guidance on the possible meanings of those legislative terms to achieve the balance desired between privacy and national security requirements in Australia when deploying CBDCs. It is suggested that internal policies are to base suspicion on factual basis for decision-makers to have reasonable grounds for contravening privacy legislations. This may be achieved through requiring decision-makers to justify their decisions and consider alternative options before infringing on privacy, thus enhancing accountability. Furthermore, it is suggested that the transparency and traceability provided by distributed ledger technologies will compel decision-makers to assess the benefits of privacy violations against their costs, promoting a balanced approach to surveillance and personal data disclosure. Originality The originality of the paper lies in its seminal analysis of the interaction of privacy an anti-money laundering laws in the context of the introduction of a central bank digital currency.
In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.
Blockchain is an emerging technology with a core emphasis on decentralization. Along with this, blockchain is immutable and follows a consensus, thereby promoting data ownership and data provenance to its users. The concept of self-sovereign identity further promotes a decentralized and distributed network. The use cases of blockchain extend beyond the cryptocurrency world. Using decentralized identifiers, smart contracts, and zero-knowledge proofs, verifiable credential models can enhance multiple areas of society. This paper focuses on establishing a communication channel between fishermen and customers within India's fisheries sector, leveraging blockchain technology. The system is designed to be fully traceable and trackable, ensuring transparency. Fishermen retain ownership of their data, empowering them to receive fair prices, while consumers gain confidence in the authenticity of their purchases. To support trust, a data-driven layer analyzes state-wise valuation and pricing trends from 2020 to 2023. These insights help verified fishermen and buyers make informed decisions with greater transparency and regional context.
The aim of this thesis is to examine the pricing and efficiency of Bitcoin options. It reviews theories of market efficiency and considers how effectively these frameworks apply to cryptocurrency markets. The thesis examines multiple option pricing models by comparing their performance for pricing Bitcoin options. Bitcoin’s high volatility and the relatively young age of its market development highlight the need to analyze how these characteristics influence both option pricing and overall market efficiency. In addition, the thesis examines the characteristics of Bitcoin options. The study provides guidelines for future research and market development, helping to build trust and support the integration of cryptocurrency derivatives into the broader financial system. Tämän opinnäytetyön tavoitteena on tarkastella Bitcoin-optioiden hinnoittelua ja markkinoiden tehokkuutta. Työssä käydään läpi markkinatehokkuuden teorioita ja arvioidaan, kuinka hyvin nämä viitekehykset soveltuvat kryptovaluuttamarkkinoihin. Opinnäytetyössä tarkastellaan useita optioiden hinnoittelumalleja vertailemalla niiden toimi- vuutta Bitcoin-optioiden hinnoittelussa. Bitcoinin korkea volatiliteetti ja sen markkinoiden suhteellisen varhaisessa kehitysvaiheessa oleva tila korostavat tarvetta analysoida, miten nämä ominaisuudet vaikuttavat sekä optioiden hinnoitteluun että markkinoiden yleiseen tehokkuuteen. Lisäksi työssä tarkastellaan Bitcoin-optioiden erityispiirteitä. Tutkimus tarjoaa suuntaviivoja tu- levalle tutkimukselle ja markkinoiden kehittämiselle, ja sen tavoitteena on lisätä luottamusta sekä tukea kryptovaluuttajohdannaisten integroitumista laajempaan finanssijärjestelmään.
Juan Sebastián Paiba Aya, Yuli Andrea Camacho Quintana
La contratación estatal en Colombia ha estado históricamente atravesada por problemáticas como la corrupción, los sobrecostos, la opacidad en los procesos y la lentitud administrativa, factores que han debilitado la confianza ciudadana en las instituciones y han limitado la eficacia en la ejecución de los recursos públicos. Frente a ello, la tecnología blockchain y los smart contracts se presentan como una alternativa innovadora al ofrecer trazabilidad, inmutabilidad y automatización en la ejecución de los contratos públicos, asegurando que las cláusulas pactadas se cumplan bajo condiciones previamente programadas y verificables.
Mohammad Sharif Karimi, Omar Esqueda, Naveen Mahasen Weerasinghe
This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability.
During the first decade of cryptocurrencies (2008–2017) there were few connections established between crypto and the conventional finance sector, but in the US in 2025 the integration of these two sectors is proceeding at speed. This paper examines one part of this integration – the centralisation of cryptocurrency trading inside of large, digital platformed exchanges, which is theorised as a shift from cryptocurrency to cryptofinance. Furthermore, the paper shows how this shift to cryptofinance has been aided by an emergent crypto-state nexus. The novel contribution of the paper is explaining how the US crypto markets have progressed from niche, relatively decentralised and blockchain-based, with little association with or regulation by nation-states, into what is now competition between FinTech-fuelled, digital platform firms that provide suites of financial services and instruments and collect fees for mediating access to the underlying blockchain markets. Empirically, the paper traces the rise of Sam Bankman-Fried’s firm, FTX, as it evolved from a small, California-based start-up running arbitrage trades in 2017 into one of the world’s largest crypto exchanges servicing over a million customers in 2022. In light of the FTX story, the paper analyses the geographical political economy of platformed cryptofinance as it struggles with both the incumbent financial sector and the US state.
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Vladyslav Prosolov, Oleksandr Kushnerov, Vladyslav Sokol, Ruslan Trofymenko
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Uisang Lee, Changhoon Chung, Junmo Lee, Sung Jun Moon
The rapid growth of Ethereum has made it more important to quickly and accurately detect smart contract vulnerabilities. While machine-learning-based methods have shown some promise, many still rely on rule-based preprocessing designed by domain experts. Rule-based preprocessing methods often discard crucial context from the source code, potentially causing certain vulnerabilities to be overlooked and limiting adaptability to newly emerging threats. We introduce BugSweeper, an end-to-end deep learning framework that detects vulnerabilities directly from the source code without manual engineering. BugSweeper represents each Solidity function as a Function-Level Abstract Syntax Graph (FLAG), a novel graph that combines its Abstract Syntax Tree (AST) with enriched control-flow and data-flow semantics. Then, our two-stage Graph Neural Network (GNN) analyzes these graphs. The first-stage GNN filters noise from the syntax graphs, while the second-stage GNN conducts high-level reasoning to detect diverse vulnerabilities. Extensive experiments on real-world contracts show that BugSweeper significantly outperforms all state-of-the-art detection methods. By removing the need for handcrafted rules, our approach offers a robust, automated, and scalable solution for securing smart contracts without any dependence on security experts.
Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.
The convergence of Artificial Intelligence (AI) and Blockchain Technology (BCT) is transforming supply-chain ecosystems by enhancing transparency, intelligence, and automation. However, existing research lacks a unified theory explaining how these technologies jointly create resilience across organizational levels. This paper extends the Strategic–Decentralized Resilience Theory (SDRT), originally developed to guide effec-tive blockchain implementation, by integrating Agentic AI capabilities to form the SDRT–Agentic AI framework. The framework conceptualizes how predictive, adaptive, and agentic (autonomous) AI capabilities reinforce SDRT’s three pillars: Strategic, Or-ganizational, and Decentralized Resilience. The framework draws on three AI modali-ties—predictive AI for strategic foresight and agility, adaptive AI for organizational learning and flexibility, and agentic AI for self-governed, trustless coordination within blockchain ecosystems. Together, these mechanisms explain how intelligent and de-centralized systems co-evolve to generate dynamic, multi-level resilience. This con-ceptual paper develops a comprehensive model and propositions describing interac-tions between AI capabilities and blockchain-based organizational structures. It con-tributes to information systems and supply-chain research by unifying two fragmented domains, AI and blockchain, under a resilience-oriented mid-range theory. Practically, the framework provides managers with a roadmap to align AI investments with de-centralized governance mechanisms, enabling proactive decision-making, adaptability, and sustainable competitiveness in increasingly autonomous digital environments.
This abstract presents a comprehensive concept that leverages the synergy of various cutting-edge technologies to assure confidentiality and integrity of health data. Internet of Things (IoT) sensors are utilized as the primary data source, enabling the continuous monitoring of patients vital signs and health parameters. To ensure the security of this sensitive health data, Blockchain infrastructure is employed. The Blockchain employs a specialized routing protocol called Improved Whale Optimized Routing to efficiently handle data transactions. This routing protocol minimizes latency and maximizes throughput, ensuring the seamless transfer of health data to the Blockchain. The security of the Blockchain is further fortified by Deep Convolutional Neural Network (DCNN) based intrusion detection system. This DCNN model is trained using Distributed Ledger Technology (DLT), which ensures data privacy and integrity by distributing the training process across a network of nodes. This collaborative approach enhances the CNN's ability to identify and respond to potential security breaches in real time. Once the health data is verified as intrusion-free, it is securely stored in the Blockchain using the shortest path routing algorithm. This guarantees that data is efficiently stored, and retrieval is expedited when needed for medical diagnosis or research. This integrated system represents a novel approach for collecting and securely storing health data, providing a robust foundation for the future of healthcare systems. It combines the power of IoT sensors, Blockchain, Deep CNN-based intrusion detection and Distributed Ledger Technology to ensure the highest standards of data security and accessibility in healthcare applications.
Cloud Computing (CC) is an excellent platform that is widely used to share information as well as services between various departments, customers, and other parties. However, during the transmission of sensitive data, effective security management is essential to ensure data privacy by avoiding unauthorised access. To address this, an efficient key agreement protocol, which is a combination of Elliptic Curve Cryptography with Zero Knowledge Proof (ECC-ZKP) model, is proposed to enhance security management in a cloud environment. The ECC-based encryption model provides equivalent security with much smaller key sizes compared to traditional cryptography approaches. Moreover, this lightweight model reduces computational load and speeds up operations in large-scale cloud environments. The integration of the ZKP model allows users to prove their identity and get access without disclosing any sensitive credentials, which leads to an increase the data confidentiality. Initially, the login and registration of users in the cloud is done by submitting all their details to the service provider. Then, a hash value and bound values are computed, and a secret key is generated by the service provider, which is sent to the client for accessing the data from the cloud. The experiment results of the proposed ECC-ZKP model achieved Makespan of 4.8 ms for 100 tasks, which is better than traditional key agreement models, such as ECC-based authenticated key agreement, respectively.
Gauhar Ali, Sajid Shah, Mohammed ElAffendi, Naveed Ahmad
Introduction Digital Twins (DT) have appeared as a significant tool in Industrial Internet of Things (IIoT) environments, allowing real-time monitoring, predictive maintenance, and maximizing device performance. However, integrating DTs with IIoT initiates serious security issues, specifically in the device’s authentication and authorization. The state-of-the-art mechanisms are exposed to insider threats, single points of failure, and privacy issues. Methods This study proposes a blockchain-based access control framework for cross-domain DTs. The blockchain (BC) integration eliminates reliance on the centralized authentication server. It uses platform verification from the manufacturer to validate IIoT device integrity and mitigate insider threats. Moreover, the authorization mechanism is implemented using smart contract and access control policies stored in BC. The proposed Non-Fungible Tokens enable role and permission delegation. Results and Discussion The integration of Hyperledger Fabric BC, platform hash verification, and NFT-based authorization in the proposed architecture enhanced its resilience against cyber-attacks i.e., replay, DoS/DDoS, insider, and spoofing attacks. Moreover, the proposed framework validates its viability with response times (approximately 300ms) for the authentication and authorization phases. Additionally, identity resolution attains 67 % depletion in latency compared to its counterpart.
Pfarelo Raliphada, Micheal O. Olusanya, Seun. Olukanmi
This study examines blockchain algorithms’ performance in a fog computing environments using data from the Bitcoin blockchain. As the demand for secure, low-latency solutions in IoT increases, blockchain offers integrity and decentralization, while fog computing ensures responsiveness. Key performance metrics like throughput, latency, and energy consumption were analyzed using statistical methods. The findings indicate significant variability in throughput and latency, along with high energy demands from traditional consensus mechanisms such as Proof of Work (PoW), making it unsuitable for fog environments. The study recommends adopting lightweight consensus protocols like Proof of Authority (PoA) and Delegated Proof of Stake (DPoS) to improve blockchain performance in edge environments.