Pablo Sotres, Alberto Carelli, M Festa, Maxime Costalonga · 9 authors
The secure and trustworthy exchange of interoperable data assets is a key enabler for the development of IoT-based data spaces. This paper presents a decentralised framework for offering management and asset sharing within trusted and interoperable data spaces, leveraging Distributed Ledger Technologies (DLTs) and the Self-Sovereign Identity (SSI) paradigm to ensure transparency, integrity, and participant self-sovereignty. The proposed architecture integrates identity management and tamper-resistant smart contracts leveraging the IOTA Tangle to support decentralised offering discovery, access control and verifiable transactions. By addressing critical challenges related to trust, interoperability, and decentralisation, the framework contributes to the technological foundations required for scalable, secure, and resilient data ecosystems.
Journal of Theoretical and Applied Information Technology
The insurance sector is being transformed through the combination of artificial intelligence (AI) and blockchain technologies. This study proposes the AI-Blockchain Hybrid Smart Contract Model (AIBSCM), which combines AI-based fraud detection with blockchain-based smart contracts to allow for automated insurance claim processing. A synthetic dataset of 1,000 insurance claims was used to train a random forest model, which achieved 92% accuracy on training data; however, real-world testing revealed difficulty in detecting fraudulent claims from under-represented categories. A blockchain simulation was conducted to demonstrate the secure storage and automated execution of claims, with smart contracts giving transparency and immutability. The architecture integrates decentralised oracles, zero-knowledge proofs (ZKPs), federated learning, and a DAO governance mechanism to provide a privacy-conscious, decentralised, and robust solution for the insurance business. Subsequent study will look at real-world deployment and integration with regulations. The integration of these technologies seeks to address traditional insurance systems' issues, such as data privacy concerns and a lack of transparency. By investigating real-world deployment and regulatory compliance, this model has the potential to transform the insurance business by delivering a safe and efficient method for dealing with false claims. This innovative method has the potential to boost client trust while also streamlining insurance company operations. Overall, the combination of blockchain and privacy-conscious technology might result in increased reliability and a transparent insurance sector.
The goal of this research is to analyze political uncertainty's short- and long-term impact on the volatility of Bitcoin throughout the US presidential election period (2023-2024), and test its value as a hedge asset in the face of rising political tensions. The GARCH-MIDAS model used here selects high-frequency daily returns on Bitcoin and low-frequency macroeconomic and political data, such as the Economic Policy Uncertainty Index (EPU), the Volatility Implied Index (VIX), and an irregular dummy variable for political events (POL_EVT). The empirical evidence depicts how Bitcoin is highly sensitive to political shocks, both sudden (short-run) and institutional (long-run), with its volatility speeding up as uncertainty increases. In contrast to traditional safe-haven securities such as gold or government bonds, Bitcoin does not exhibit hedging behavior during times of political turmoil. Instead, it is a high-risk speculation asset, responding in real-time but destabilizing to evolving political events. Moreover, the GARCH-MIDAS model proved to be outstanding in capturing the time and non-linear impacts of uncertainty compared to standard models, buttressing the importance of including political factors when studying the volatility dynamics of cryptocurrencies.
Axel Flodmark, Rasmus Samuelson, David Hasselquist, Martin Arlitt · 5 authors
Cryptocurrencies’ pseudonymity property poses regulatory challenges and has attracted illicit actors that try to avoid oversight. To counter this, the U.S. Treasury’s Office of Foreign Assets Control (OFAC) sanctions individuals and entities using Bitcoin for cybercrime, terrorism financing, and other illicit activities. However, the effectiveness of these measures remains uncertain. This study analyzes over 13 million Bitcoin transactions linked to sanctioned entities, tracing fund flows and exchange interactions. We find that ~175,000 BTC was moved before sanctions took effect, with only 50 BTC remaining post-sanction, indicating preemptive fund displacement. Cybercrime-linked addresses accounted for the largest transfers—sometimes exceeding $1 billion—while sanctioned entities favored large, direct transactions to exchanges. Despite activity dropping immediately after sanctions took effect, some entities continued transacting for up to 1,500 days, exposing enforcement gaps. Our findings highlight key challenges in sanction enforcement, including delayed restrictions, exchange compliance gaps, and strategic fund movements. These insights inform policymakers and regulators seeking to strengthen cryptocurrency financial controls.
Gabriel Babatunde Iwasokun, Oluwaseyi Segun, Samuel Oluwatayo Ogunlana, Michael Adegoke · 6 authors
The integration of Internet of Things (IoT) devices into modern payment systems has introduced innovative functionalities, but also significant security and performance challenges. IoT devices, such as smart sensors, wearables, and automated vending machines, are typically resource-constrained yet handle sensitive financial transactions that demand robust security mechanisms. Conventional cryptographic solutions are often unsuitable for these environments due to their high computational and memory requirements. This paper presents the design of a lightweight blockchain-based model to secure IoT payment systems by leveraging the Ethereum blockchain and AES-128 encryption. The blockchain token is encrypted with AES-128 to add layer of security before being stored in a database. The model is designed to employ a decentralised digital ledger to record and validate transactions without a central authority, and the transaction is grouped into a block and linked to the preceding block through cryptographic hashes. The chain of blocks forms an immutable record that enhances transparency and security, and the distributed nature of blockchain networks, wherein multiple participants validate each transaction, minimises the risk of fraudulent activities while ensuring consensus is achieved through predefined protocols. Analysis of results from the implementation established the minimization of computational overhead and robust security measures, and was particularly beneficial where the scalability of decentralized systems is required alongside heightened security protocols.
Meeting global forest restoration targets by 2030 requires a transition from labor-intensive and opaque practices to scalable, intelligent, and verifiable systems. This paper introduces a cyber–physical digital twin architecture for forest restoration, structured across four layers: (i) a Physical Layer with drones and IoT-enabled sensors for in situ environmental monitoring; (ii) a Data Layer for secure and structured transmission of spatiotemporal data; (iii) an Intelligence Layer applying AI-driven modeling, simulation, and predictive analytics to forecast biomass, biodiversity, and risk; and (iv) an Application Layer providing stakeholder dashboards, milestone-based smart contracts, and automated climate finance flows. Evidence from Dronecoria, Flash Forest, and AirSeed Technologies shows that digital twins can reduce per-tree planting costs from USD 2.00–3.75 to USD 0.11–1.08, while enhancing accuracy, scalability, and community participation. The paper further outlines policy directions for integrating digital MRV systems into the Enhanced Transparency Framework (ETF) and Article 5 of the Paris Agreement. By embedding simulation, automation, and participatory finance into a unified ecosystem, digital twins offer a resilient, interoperable, and climate-aligned pathway for next-generation forest restoration.
Purpose: This study explores the transformative impact of financial technology (fintech) on the global financial services industry, focusing on innovations, regulatory implications, and challenges. The research aims to identify key technological disruptions, examine the regulatory landscape, and highlight opportunities and risks introduced by fintech. Methodology/approach: A Systematic Literature Review (SLR) was conducted using SCOPUS, IEEE Xplore, and ScienceDirect. Following a structured protocol, 153 peer-reviewed articles (2014–2019) were analysed through thematic and meta-analytical approaches. The study adopted an interpretative philosophy and used the PICOC framework to refine search precision and synthesis. Results/findings: The analysis reveals fintech’s disruptive innovations in financing and payment systems, such as peer-to-peer (P2P) lending, crowdfunding, blockchain-enabled transactions, and mobile payments. These services have enhanced financial inclusion, operational efficiency, and customer accessibility. Regulatory frameworks have evolved in parallel, though challenges remain in addressing moral hazard, cybersecurity, and compliance. Geographically, Asia, particularly China and Indonesia, leads fintech research and implementation. Conclusion: Fintech has significantly reshaped financial ecosystems by enabling decentralized financial services, accelerating digital transactions, and fostering inclusivity. However, cybersecurity risks, limited regulatory clarity, and uneven global adoption continue to impede its sustainable integration. Limitations: The study is limited to English-language literature from 2014–2019 and may not capture recent post-pandemic developments or region-specific innovations in Islamic or informal economies. Contribution: This paper contributes a comprehensive synthesis of fintech’s evolution, identifies existing gaps, and offers insights for policymakers, financial institutions, and researchers to foster a balanced, secure, and innovative financial environment.
Transport Infrastructure Ireland (TII) commissioned a cooperative intelligent transport system (C-ITS) pilot. Although the European C-ITS Security Credential Management System (EU CCMS) and European Telecommunications Standards Institute Public Key Infrastructure (ETSI PKI) standards offer a foundation for secure Vehicle-to-everything (V2X) communication, challenges persist in scalability, latency, revocation, and misbehavior detection. This work proposes a hybrid framework, with improvements including blockchain-based revocation, decentralized trust models, and privacy preservation using pseudonym rotation and zero-knowledge proofs, thus extending existing standards.
This paper introduces SAT-IOTA, a lightweight and AI-driven cybersecurity framework designed for blockchain-powered satellite infrastructures. Unlike traditional detection approaches, SAT-IOTA employs predictive anomaly analytics combined with a Sliding Window (SW) machine learning mechanism to proactively identify and mitigate security threats in space-air-ground integrated networks (SAGINs). The proposed framework integrates IOTA distributed ledger technology (DLT) for secure, decentralized telemetry data management, tokenized satellite components, and resilience against cyber-physical attacks. Through a custom-built testbed with Hornet nodes, we evaluate the frameworks performance under denial-of-service (DoS) scenarios, achieving 97% prediction accuracy and an F-measure of 80%. The results confirm that SAT-IOTA enhances space system security by combining blockchain-driven trust with AI-based anomaly prediction, offering a scalable and resource-efficient solution for next-generation satellite communications.
With the rise of cryptocurrencies, illicit activities such as money laundering, fraud, and Ponzi schemes have gained attention. Traditional methods using graph neural networks (GNNs) to detect illicit transactions treat the entire transaction network as input, which works well on small networks but struggles with large-scale blockchain data. To address this limitation, the authors propose a neighborhood subgraph-based method that combines GCN and LSTM. The GCN captures information from neighboring nodes for each transaction, enhancing the understanding of the network structure, while the LSTM tracks the sequence and variations of fund flows. Experimental results show that by using 3-hop neighborhood subgraphs, the method outperforms other baseline models while requiring data from only an average of 80 nodes, thereby significantly improving efficiency compared to methods that process the entire transaction network.
This Research paper attempts to examine and analyse the legal nature and law which govern virtual property, covering the concept of ownership, transfer, and regulatory challenges within the metaverse. This Research paper aims to set-out the struggles of traditional legal framework to adapt to the new digital environment consisting of technologies such as blockchain, artificial intelligence (AI), augmented and virtual reality (AR/VR), 3D modelling, and edge computing converge to form the metaverse. The study explains blockchain technology, as it reinforces non-fungible tokens (NFTs) which is the key standard for virtual ownership. It also attempts to analyse how existing legal framework in India for property laws, such as the Transfer of Property Act 1882[1] and the Sale of Goods Act 1930[2], could bring virtual assets under its legal parameters. A comparative analysis of the UK, US, EU, and Indian legal frameworks shows how different legal approaches helps in classification of digital assets. The UK Law Commission’s recommendation demonstrates a progressive shift toward recognising virtual property rights by introducing a new category of “digital objects”.[3] The Research paper highlights the inadequacy of existing property laws for resolving the exclusive cross-jurisdictional and ownership challenges posed by digital environments, concluding that just providing conceptual foundation is not enough. It advocates for a harmonised global governance framework integrating statutory law, soft law principles like the UNIDROIT Principles of International Commercial Contracts[4], and platform-specific regulation to ensure certainty, accountability, and protection of digital ownership.
Elham Hashemi Nezhad, Antonio Di Maio, Torsten Braun
The orchestrators’ deployment problem presents numerous challenges in 6G Network Radio Access Networks due to their large-scale, dynamic conditions, and variable user demands. Most works propose single- or hierarchical-orchestrator solutions, which offer poor resiliency, high signaling overhead, and slow adaptation to variable network dynamics. To tackle these challenges, we propose an online, data-driven, fully decentralized, Multi-Agent Reinforcement Learning (MARL)-based, self-organization orchestrator deployment system for 6G networks, which jointly optimizes the tradeoff between user throughput and fairness, based on time-varying system conditions. In the proposed approach, a flexible variable number of decentralized, cooperative, peer self-organization agents autonomously adapt their associated orchestrator’s deployment location and activity to optimize network operation, without requiring centralized coordination. Simulations show improvements of up to 77% in user throughput compared to Hierarchical and Single Orchestrator baselines in a broad range of realistic scenarios.
Jean Baudrillard's (1929–2007) theoretical writings are applied to an examination of today's global virtual economy and society. New advances in virtual culture, which flourished during and after the COVID-19 pandemic—esp., metaverses (immersive virtual worlds), non-fungible tokens, and deepfakes (synthetic media)—are discussed to show the prescience of Baudrillard's theory for how our global consumer society of the image is now defined by the problem of simulation. Baudrillard is shown to have theorized important trends and phenomenon in our contemporary global hyperculture that have hitherto been neglected: non-communication, anti-work, and anti-consumption are, among others, explained as developing phenomena because they are pathologies of a new nihilism, a hatred of capitalism, that is not realized through destruction, but through the simulation and deterrence that now defines contemporary global culture and society.
This systematic review examines how elite athletes are leveraging digital platforms, generative artificial intelligence (AI), and blockchain to build autonomous brands, bypass traditional sport gatekeepers, and develop athlete-owned business models. Drawing on 47 peer-reviewed studies (2016-2025), we synthesise evidence across five domains: athlete branding and self-production, disintermediation, platform-enabled empowerment, AI-driven content innovation, and emerging commercial structures. The findings reveal a decisive shift in sport's power balance, with athletes acting as media producers, cultural influencers, and entrepreneurial actors. Digital platforms enable direct-to-fan engagement, while AI tools lower content production costs whilst personalising interactions and extend global reach. Blockchain facilitates decentralised monetisation and data sovereignty, supporting ventures such as athlete-owned leagues and non-fungible tokens. However, these developments embed new dependencies on platform algorithms and volatile digital markets. From a platform capitalism perspective, athlete autonomy is constrained by corporate-controlled infrastructures; from a value co-creation lens, fan relationships become participatory spaces for shared cultural and commercial value creation. The review highlights governance challenges, including ethical implications of synthetic media, data ownership, and the regulation of AI-enabled branding ecosystems. We argue that sport governance must evolve from a control-oriented model to one that positions athletes as co-creators of value and strategic partners in decision-making. Future research should address equity in digital visibility and sustainable athlete-led business ecosystems. Governance mechanisms that reconcile technological opportunity with autonomy protection should be explored as well. Athletes are no longer peripheral actors in sport's commercial order, they are emerging as its architects, with significant implications for the future of sport governance.
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK programs remains challenging: unlike conventional software development, ZK programming manifests a fundamental paradigm shift from \textit{imperative computation} to \textit{declarative verification}. This process requires rigorous reasoning about finite field arithmetic and complex constraint systems (which is rare in common imperative languages), making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages, their effectiveness for ZK programming, where correctness hinges on both language mastery and constraint-level reasoning, remains unexplored. To address this gap, we propose \textsc{ZK-Eval}, a domain-specific evaluation pipeline that probes LLM capabilities on ZK programming at three levels: language knowledge, algebraic primitive competence, and end-to-end program generation. Our evaluation of four state-of-the-art LLMs reveals that while models demonstrate strong proficiency in language syntax, they struggle when implementing and composing algebraic primitives to specify correct constraint systems, frequently producing incorrect programs. Based on these insights, we introduce \textsc{ZK-Coder}, an agentic framework that augments LLMs with constraint sketching, guided retrieval, and interactive repair. Experiments with GPT-o3 on Circom and Noir show substantial gains, with success rates improving from 20.29\% to 87.85\% and from 28.38\% to 97.79\%, respectively. With \textsc{ZK-Eval} and \textsc{ZK-Coder}, we establish a new basis for systematically measuring and augmenting LLMs in ZK code generation to lower barriers for practitioners and advance privacy computing.
Open access
2 source records
Mathematics, Computing, and Information Processing
Smart contracts are a key part of blockchain applications, and attackers can exploit them to manipulate contract behaviour or steal assets. Smart contracts often contain security vulnerabilities, either accidentally introduced by developers or due to flawed business logic. In this paper, we focus on finding an optimal Machine Learning based framework for detecting vulnerable smart contracts by analysing the smart contracts as embedding vectors. CodeBERT, a pre-trained transformer model, is used for feature extraction in the proposed framework. The framework has shown approximately 97% accuracy in detecting smart contracts that contain various vulnerabilities. Additionally, the research explores the performance of CodeBERT variants for this task. The results of the experiments have proven the favourability of this framework in detecting vulnerable smart contracts.
Blockchain technology has emerged as a transformative force across a multitude of sectors, offering decentralized, transparent, and tamper-proof solutions to conventional problems in data management, finance, supply chain, healthcare, and beyond.Initially popularized through cryptocurrencies, blockchain has since evolved into a broader infrastructure supporting smart contracts, decentralized applications (dApps), and Web3 ecosystems.This survey provides a comprehensive overview of blockchain technology, outlining its fundamental principles including distributed ledgers, consensus mechanisms, cryptographic security, and decentralization.We critically examine various blockchain architectures such as public, private, and consortium blockchains, and explore their relative strengths and limitations.The paper further delves into current trends, emerging use cases, scalability challenges, interoperability issues, and security concerns.By synthesizing recent academic and industry developments, this survey aims to provide researchers and practitioners with a holistic understanding of blockchain's capabilities, current limitations, and future directions.
Web3 applications require execution platforms that maintain confidentiality and integrity without relying on centralized trust authorities. While Trusted Execution Environments (TEEs) offer promising capabilities for confidential computing, current implementations face significant limitations when applied to Web3 contexts, particularly in security reliability, censorship resistance, and vendor independence. This paper presents dstack, a comprehensive framework that transforms raw TEE technology into a true Zero Trust platform. We introduce three key innovations: (1) Portable Confidential Containers that enable seamless workload migration across heterogeneous TEE environments while maintaining security guarantees, (2) Decentralized Code Management that leverages smart contracts for transparent governance of TEE applications, and (3) Verifiable Domain Management that ensures secure and verifiable application identity without centralized authorities. These innovations are implemented through three core components: dstack-OS, dstack-KMS, and dstack-Gateway. Together, they demonstrate how to achieve both the performance advantages of VM-level TEE solutions and the trustless guarantees required by Web3 applications. Our evaluation shows that dstack provides comprehensive security guarantees while maintaining practical usability for real-world applications.
Muhammad Haroon Tariq, Uswa Ihsan, Zaenal Alamsyah
Ownership rights related to land and property represent a highly contentious matter in areas across Pakistan because female inheritors struggle to assert their property rights due to cultural practices along with unclear procedures and traditional document systems. The present government-controlled systems demonstrate inadequate proficiency along with safety protocols to execute fair inheritance distribution, mainly impacting marginalized populations. This research introduces a blockchain system known as the Land Registration and Inheritance Automation System (LRIAS) which prioritizes the female protection of inheritance privileges. The proposed system includes digitalizing the traditional paper-based land registration and inheritance process. The system ensures blockchain security through the implementation of MetaMask together with Web3.js for Ethereum transactions. The blockchain system distributes inheritances through programmed agreements which follow Shariah validation rules. The LRIAS establishes permanent and free-version records that show who owns land and who the legal heirs are. The system enables women to access their inheritance records through verifiable reliable data which cannot be altered. Through the system, authorities can verify inheritance claims and execute them without bureaucratic interference, which minimizes both legal disputes and family conflicts. Experimental tests show that the LRIAS succeeds in safeguarding women’s land inheritance claims and increasing confidence in legal inheritance procedures.
Internet of Underwater Things (IoUT) introduces critical security challenges, especially for protecting distributed infrastructures in resource-constrained environments. Conventional asymmetric and centralized authentication models are unsuitable due to computational and communication overhead, while symmetric approaches lack robustness without trusted storage or hardware. We propose a non-interactive, asynchronous authentication protocol based on NIZKP, combining PUFs-derived secrets with decentralized identifiers on a distributed ledger. This approach enables direct node authentication with cryptographically verifiable identity binding, minimal resource usage, offline verification, and full support for asynchronous operation in constrained environments. The protocol is formally analysed and implemented on COTS hardware without additional secure components. Evaluation shows low energy consumption (827.2 mJ), minimal communication overhead (113 B, 1.513s, 817.9 mJ), and reasonable execution times (worst case ≈ 5.310s), outperforming state-of-the-art solutions in the first four metrics.
Haruki Kurisaka, Yue Su, Phi Le Nguyen, Kien Nguyen · 5 authors
Abstract The integration of IoT with blockchain technology enhances security and privacy through decentralized, trust-based systems, addressing challenges like single points of failure and limited scalability in traditional IoT architectures. This study evaluates the performance of Ethereum-based IoT systems using resource-constrained devices (Raspberry Pi 4 and Raspberry Pi 3) on a private blockchain. Performance metrics, including CPU, memory, disk usage, power consumption, and latency, were analyzed across three consensus mechanisms: Proof-of-Work (PoW), Proof-of-Authority (PoA), and Proof-of-Stake (PoS). To address the blockchain’s latency performance, we introduced the metrics Transaction-oriented latency (ToL) and Block-oriented latency (BoL) to characterize latency under PoS, capturing the distinctive dynamics of PoS. Our findings show that PoA achieves the lowest resource consumption, with CPU usage reduced by 98% compared to PoW and 20% compared to PoS, and power consumption decreased by 50% from PoW and 14% from PoS. Further, to assess blockchain scalability, we varied transaction transmission rates under PoA, identifying its impact on performance. These findings provide practical guidance for optimizing consensus mechanisms in resource-constrained IoT-blockchain systems.
Majd AbedRabbo, Zeina AlMalak, Fiona Ellis‐Chadwick, Jοãο S. Oliveira
ABSTRACT This paper explores consumers' drivers and motivations behind luxury‐fashion non‐fungible tokens (NFTs) ownership and the implications of the potential ownership of these NFTs on the purchase intentions of physical luxury products of the same brand. Hitherto, little research has been conducted on the consumer's perception of ownership and its effect on physical product purchases. Following the Self Determination Theory (SDT), a two‐step qualitative research approach is implemented due to the lack of empirical research in this area. This study focuses on luxury fashion NFTs and targets millennials and generation Z consumers. A total of 4 focus groups (25 participants) and 6 semi‐structured interviews were conducted to address the objectives of this research. Using thematic analysis, the study identifies 5 key drivers behind NFTs ownership: authenticity, exclusivity, scalability, affordability, and digital literacy. Scalability of luxury fashion NFTs valuation is found to be a critical driver of consumers' ownership intentions. Similarly, digital literacy was identified as a new driver of intentions of ownership of luxury NFTs considering its effect on consumers' social status. Finally, depending on consumers' lifestyle, ownership of luxury fashion NFTs is argued to have a mixed effect on the intentions of ownership of physical luxury products. This research contributes to the development of the understanding of the emerging concept of luxury NFTs and their profound influence on consumers' perceptions of ownership and purchase intentions for physical luxury products.
Open access
Consumer Behavior in Brand Consumption and Identification