Carlos Beis-Penedo, Rebeca P. Díaz-Redondo, Ana Fernandez-Vilas, Manuel Fernández‐Veiga · 5 authors
Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.
Roy Kanavheti, Wellington Makondo, Wellington Simbarashe Manjoro
Academic qualification forgery poses a major concern for higher learning institutions, employers, and regulatory authorities throughout the world. In Zimbabwe, the increase in the level of fake degrees has greatly eroded trust in the education industry. Conventional verification processes are time-consuming, manual, and highly vulnerable to tampering. This paper introduces a hybrid blockchain-based and AI-enabled academic qualification verification platform to fight the problems. A prototype was implemented integrating various artificial intelligence algorithms including Convolutional Neural Networks (CNN), Autoencoder, Random Forest, and One-Class Support Vector Machines (SVM) with Algorand blockchain for secure, transparent, and decentralized record keeping. Zero-Knowledge Proofs (ZKPs) were utilized to ensure privacy. The system was tested based on a mixed-methods and Design Science Research (DSR) approach across many performance measures. Results show fraud detection accuracy, near-instantaneous verification speed, and satisfaction with privacy standards. The proposed system provides a sustainable and scalable framework for enhancing academic integrity in Zimbabwe's higher education system and primes the region for digital transformation of education.
Encouraging just, secure, and open election processes is a fundamental aspect of any democratic culture. Traditional and even modern electronic voting systems are plagued by persistent issues like the failure to provide anonymity for voters, forgery risks, scalability, and the absence of verifiable trust. This paper proposes a blockchain-based digital voting framework designed to address these systemic limitations by leveraging distributed ledger technology and smart contracts. The proposed solution offers end-to-end verifiability, vote immutability, and decentralized auditing mechanisms through a mobile-accessible platform built on Ethereum using Solidity and Hardhat, with Node.js and React.js for frontend interfacing. Experimental results demonstrate improved system scalability, resistance to tampering, and support for remote voting, while maintaining ballot privacy and affordability. The research also evaluates key performance indicators under various test scenarios, establishing the system’s effectiveness and practical relevance in real-world electoral environments.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
El objetivo de este trabajo es analizar la naturaleza jurídica de la criptomoneda Bitcoin en el ordenamiento jurídico chileno. Para ello, se realiza una aproximación a la figura de los criptoactivos, para luego describir las principales características del bitcoin. Posteriormente, se realiza un análisis dogmático y normativo tanto de la literatura especializada como de la legislación vigente aplicable a la materia. Considerando las particulares características del Bitcoin, así como lo expresado por la doctrina nacional y comparada, y especialmente, los términos de nuestro Código Civil y de la Ley 21.521 (Ley Fintec), es dable sostener la imposibilidad de identificar al Bitcoin con alguna categoría de lege lata que permita explicar integralmente la figura, por lo que es más apropiada su calificación de bien sui generis a la luz del ordenamiento jurídico nacional.
As the climate crisis intensifies and urban populations swell, megacities face compounding threats from carbon emissions, urban heat islands (UHIs), and ecosystem degradation. While nature-based solutions (NbS) offer a promising response through ecological restoration and carbon sequestration, current NbS deployments are often fragmented, non-adaptive, and lack quantitative optimization. This research presents a cutting-edge, artificial intelligence (AI)-driven architecture that operationalizes NbS through a scalable, data-intensive framework. It integrates deep learning (DL) for satellite-derived land classification, graph neural networks (GNNs) for spatial co-benefit mapping, and reinforcement learning (RL) with dynamic reward weighting to optimize intervention strategies in real time. Life cycle assessment (LCA) and ecosystem service valuation modules are embedded to ensure holistic, cross-sectoral impacts. The architecture is deployed in a high-resolution case study of Dhaka, Bangladesh, a climate-vulnerable megacity, achieving over 8,500 metric tons of modeled annual carbon sequestration, 2.1°C reduction in UHI intensity, and quantifiable gains in urban biodiversity and flood mitigation. The system ingests multi-source data, including Sentinel-2, LiDAR, and CMIP6 climate projections, while leveraging federated learning to ensure decentralized, privacy-preserving optimization across municipal zones. A carbon market compatibility layer, aligned with Verra, UN-REDD+, and Article 6 frameworks, enables eligibility for climate finance and offsets. The approach also integrates social equity metrics and indigenous ecological knowledge to prioritize interventions in marginalized zones. This work delivers a first-of-its-kind decision-support platform for AI-optimized NbS that is globally scalable, policy-aligned, and climate-finance ready. It represents a paradigm shift from heuristic-based planning to algorithmically adaptive ecosystem engineering, accelerating progress toward net-zero emissions, SDG convergence, and resilient urban futures. The framework is poised to inform urban sustainability strategies worldwide, offering a replicable model for AI-governed environmental transformation in the age of planetary emergency.
The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP (Hou et al., 2025), A2A (Habler et al., 2025), ACP (Liu et al., 2025), and Cisco's AGP (Edwards, 2025) -- cannot address at scale. We present the Nanda Unified Architecture, a decentralized framework built around three core innovations: fast DID-based agent discovery through distributed registries, semantic agent cards with verifiable credentials and composability profiles, and a dynamic trust layer that integrates behavioral attestations with policy compliance. The system introduces X42/H42 micropayments for economic coordination and MAESTRO, a security framework incorporating Synergetics' patented AgentTalk protocol (US Patent 12,244,584 B1) and secure containerization. Real-world deployments demonstrate 99.9 percent compliance in healthcare applications and substantial monthly transaction volumes with strong privacy guarantees. By unifying MIT's trust research with production deployments from Cisco and Synergetics, we show how cryptographic proofs and policy-as-code transform agents into trust-anchored participants in a decentralized economy (Lakshmanan, 2025; Sha, 2025). The result enables a globally interoperable Internet of Agents where trust becomes the native currency of collaboration across both enterprise and Web3 ecosystems.
Tri Stiyo Famuji, Alya Masitha, Maulana Muhammad Jogo Samodro, Galih Pramuja Inngam Fanani · 5 authors
Perkembangan teknologi genomik meningkatkan urgensi penanganan kerentanan sistem terpusat dalam mengelola data genetik manusia. Penelitian ini merancang sistem terdesentralisasi berbasis blockchain dan IPFS untuk meningkatkan keamanan, integritas, dan aksesibilitas data. Blockchain digunakan untuk pencatatan transaksi yang immutable dan manajemen akses dinamis melalui smart contract, sedangkan IPFS menyediakan penyimpanan terdistribusi berbasis hash kriptografik (CID). Desain hybrid memisahkan penyimpanan data mentah (terenkripsi homomorfik) di IPFS dengan manajemen akses di blockchain Ethereum. Antarmuka pengguna berbasis React.js dan Web3.js memungkinkan pengunggahan data terenkripsi, permintaan akses berbasis peran, dan pemantauan audit sesuai standar GDPR/HIPAA. Pengujian membuktikan sistem ini efektif mencegah akses ilegal dan memastikan keterlacakan data. Tantangan seperti efisiensi energi dan kepatuhan regulasi diatasi melalui sharding, protokol layer-2, serta mekanisme penghapusan data selektif. Kerangka kerja ini mendukung kolaborasi riset genomik lintas institusi secara aman. Penelitian lanjutan akan difokuskan pada optimasi efisiensi komputasi dan perluasan skala data.
Joel Sepúlveda, Amanda Lemette, Karla Ohler-Martins
The rise of cryptocurrencies and decentralised finance (DeFi) has fuelled a fast-growing digital assets economy with major environmental and financial implications. Proof-of-work (PoW) systems like Bitcoin demand high energy and emit large volumes of CO₂, while proof-of-stake (PoS) alternatives such as Ethereum and Cardano significantly reduce environmental costs. This paper analyses seven major crypto projects: Ethereum, Uniswap, Aave, Maker, Cardano, XRP, and Stellar. It focuses on their energy consumption, financial performance, and sustainability. The study proposes a novel sustainability scoring framework to support ESG-aligned investment and regulatory design. While PoW offers unmatched security, its environmental toll is unsustainable. PoS models show promise but face governance and scalability concerns. The study highlights the urgent need for sustainable innovation and regulatory differentiation to align crypto markets with climate goals, investor expectations, and long-term economic viability.
With the advancement of digital payment technologies, central banks worldwide have increasingly begun to explore the implementation of Central Bank Digital Currencies (CBDCs). This paper presents a comprehensive review of the latest developments in CBDC system design and implementation. By analyzing 135 research papers published between 2018 and 2025, the study provides an in-depth examination of CBDC design taxonomy and ecosystem frameworks. Grounded in the CBDC Design Pyramid, the paper refines and expands key architectural elements by thoroughly investigating innovations in ledger technologies, the selection of consensus mechanisms, and challenges associated with offline payments and digital wallet integration. Furthermore, it conceptualizes a CBDC ecosystem. A detailed comparative analysis of 26 existing CBDC systems is conducted across four dimensions: system architecture, ledger technology, access model, and application domain. The findings reveal that the most common configuration consists of a two-tier architecture, distributed ledger technology (DLT), and a token-based access model. However, no dominant trend has emerged regarding application domains. Notably, recent research shows a growing focus on leveraging CBDCs for cross-border payments to resolve inefficiencies and structural delays in current systems. Finally, the paper offers several forward-looking recommendations for future research.
This study presents a comparative evaluation of five widely adopted blockchain consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—based on energy efficiency and performance metrics. As blockchain systems scale into broader applications, key indicators such as transaction throughput (TPS), latency, security, centralization risk, and scalability become increasingly critical. Each algorithm is assessed through theoretical analysis, empirical data, and simulation-based studies found in the literature. The findings indicate that Proof of Work is the most secure, but it does not environmentally sustainable due to its energy needs. Proof of Stake and Delegated Proof of Stake are more energy-efficient and have a high throughput, but it can lead to centralization. A Practical Byzantine Fault Tolerant consensus algorithm is great in permissioned networks, but it doesn't scale well. Furthermore, those networks aren't really decentralized. Proof of Authority is the most environmentally sustainable option, but that energy efficiency comes at the cost of decentralization. This research highlights the lack of sustainable consensus mechanisms, that is, consensus protocols that are both secure and scalable and environmentally sustainable—as a gap that future research can address.
Blockchain and smart contracts are widely used in IoT access control to create decentralized, trustworthy environments for secure access and record management. However, their application introduces a dual challenge: The transparency of blockchain and the use of addresses as identifiers can expose account privacy. To tackle this issue, this paper proposes a blockchain-based IoT access control system that enhances account anonymity and preserves privacy, particularly regarding user behavior, habits, and access records through the use of zero-knowledge proofs. The system incorporates an access control mechanism that combines access control lists with capability-based access control, enabling ownership verification of access rights without disclosing identity information. To evaluate the system’s feasibility, we conduct experiments in a smart building scenario, including both qualitative comparisons with existing methods and quantitative analyses of performance in terms of time, space, and gas consumption. The results indicate that our scheme achieves the best time efficiency in the proof generation and authorization phases, completing them in just 7 and 10 s, respectively—representing half the time required by the second-best approach. These findings underscore the system’s superior cost efficiency and enhanced security compared to existing solutions.
This study applied the Spatial Durbin model (SDM) to examine the regional impact of fiscal federalism on green financing and environmental quality in China between 2000 and 2020. Using principal component analysis (PCA) in STATA, the environmental quality index and green financing (GF) model were created. The fully modified ordinary least squares (FMOLS) and dynamic panel ordinary least squares (DOLS) approaches were used to assess the baseline model’s robustness. The findings indicate that there is a beneficial and noteworthy impact on environmental quality from the decentralization of fiscal expenditure and GF. Moreover GF is positively and significantly correlated with green technological innovation (GTI) and fiscal decentralization (FD), both of which are identified as GF accelerators. The association between the environment and the interaction impact of GF and green technology (GT) is favorable and noteworthy. Based on this study, the Chinese Government should expedite the decentralization process to improve GF and, eventually, environmental quality. Fiscal expenditure decentralization (F.DE) and GT play a significant role in promoting environmentally friendly technologies, optimal energy use, and innovations in the effort to create the least polluting economy.
ABSTRACT As digital environments continue to expand and blockchain technology advances, luxury brands are increasingly focusing on enhancing brand value and communication with consumers through digital assets within the metaverse. Drawing on value–attitude–behavior (VAB) theory, this study examines how luxury brands are leveraging non‐fungible tokens (NFTs) in the metaverse to influence consumer attitudes and behaviors, particularly resistance to negative information and brand purchase intention. In Study 1, 519 metaverse users were surveyed to empirically test the relationships between NFT attributes, value perceptions, brand attitudes, and consumer behavior. In Study 2, a second survey was conducted with 286 luxury consumers to compare the brand purchasing experience. We find that NFT attributes—namely, authenticity and scarcity—positively impact value perceptions, while hedonic, social, and epistemic value positively affect brand attitude, resistance to negative information, and brand purchase intention in the physical world. Finally, we provide strategic insights for luxury brands seeking to enhance consumer engagement in digital environments.
Open access
2 source records
Consumer Behavior in Brand Consumption and Identification
As AI models become ubiquitous in our daily lives, there has been an increasing demand for transparency in ML services. However, the model owner does not want to reveal the weights, as they are considered trade secrets. To solve this problem, researchers have turned to zero-knowledge proofs of ML model inference. These proofs convince the user that the ML model output is correct, without revealing the weights of the model to the user. Past work on these provers can be placed into two categories. The first method compiles the ML model into a low-level circuit, and proves the circuit using a ZK-SNARK. The second method uses custom cryptographic protocols designed only for a specific class of models. Unfortunately, the first method is highly inefficient, making it impractical for the large models used today, and the second method does not generalize well, making it difficult to update in the rapidly changing field of machine learning. To solve this, we propose ZKTorch, an open source end-to-end proving system that compiles ML models into base cryptographic operations called basic blocks, each proved using specialized protocols. ZKTorch is built on top of a novel parallel extension to the Mira accumulation scheme, enabling succinct proofs with minimal accumulation overhead. These contributions allow ZKTorch to achieve at least a $3\times$ reduction in the proof size compared to specialized protocols and up to a $6\times$ speedup in proving time over a general-purpose ZKML framework.
The present study aims to analyze the return and risk performance ofselected cryptocurrencies in orderto find out which cryptocurrencies have small risks and large returns. The research time period is 2017 to 2022. The objective of this research is to compute and compare the risk and return performance of the selected cryptos. The findings of this research are that the risk is very high in Bitcoin compared to Ethereum, as shown in the data analysis, and Ethereum has high returns. Before starting an investment, it is better to look at the ability of Cryptocurrency assets to minimize risks and make sure that the investment objectives are for the long and short term.
Bitcoin is one of the most prominent digital assets in the modern financial era due to its high volatility and huge profit potential. However, its extreme price volatility also makes it a high-risk asset, so a reliable forecasting approach is needed to help investors make more rational decisions. This study aims to forecast Bitcoin price using the Moving Average (MA) method, specifically MA3, by utilizing monthly historical data of Bitcoin price in USD currency obtained from investing.com website. The MA3 method was chosen for its ability to smooth out short-term fluctuations and identify the direction of price trends. The forecasting process is performed by calculating the average of the last three months' prices for each point in time and compared to the actual price to evaluate its accuracy. The evaluation is done using various prediction error metrics, namely Error, Absolute Error, Squared Error, and Percentage Error. The results of the analysis show that the MA method provides a fairly representative picture of price trends and can be used as an early indicator in short-term investment strategies. Thus, the Moving Average method proves to be a simple but effective prediction tool, especially for novice investors in the dynamic crypto asset market.
Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.
Open access
Artificial Intelligence in Healthcare and Education
Kaharuddin Kaharuddin, Asep Saepudin Jahar, Arta Amaliah Nur Afifah
Blockchain technology introduces an innovative approach to waqf governance by incorporating smart contracts based on distributed ledger technology and encryption security. This advancement enhances administrative services and strengthens public trust in waqf management. This research uses a qualitative descriptive approach based on a literature review to explore the benefits of applying blockchain technology in waqf governance. The findings reveal that blockchain technology significantly improves the previously unstructured waqf governance system. By enabling encrypted and decentralised transaction recording, blockchain ensures transparent management of waqf funds, facilitates efficient tracking of fund status, and prevents data manipulation. Additionally, blockchain enhances the efficiency and security of transactions, paving the way for an innovative and digital waqf ecosystem. The technology also promotes financial inclusion by expanding public access to participate in waqf initiatives. This broader participation contributes to poverty alleviation (SDG 1) and reduces economic disparities (SDG 10) through waqf funds’ transparent and accountable distribution. Integrating blockchain technology into waqf governance offers a transformative solution for advancing transparency, efficiency, and inclusivity in Sharia financial systems.
Zahiduzzaman Zahid, Ruhul Amin, Ibrahim Khalil, Basharat Ali Khan Mohammed · 5 authors
This paper investigates the intersection of MiCA and Islamic finance by conducting a comparative regulatory analysis, focusing on core areas such as stablecoin structures, decentralized finance (DeFi), smart contracts, and ethical governance. It critically examines MiCA’s reserve and redemption frameworks for asset-referenced tokens (ARTs) and e-money tokens (EMTs) against Shariah mandates of asset-backing, risk-sharing, and prohibition of riba (interest) and gharar (excessive uncertainty). The analysis further explores the legal and ethical tensions between MiCA’s treatment of decentralized assets and Islamic jurisprudence, especially regarding profit-sharing models like Mudarabah and Musharakah. It highlights challenges Islamic fintechs operating within the EU face, including the lack of recognition for Shariah boards and faith-based audit systems under MiCA. The study concludes by proposing policy recommendations for greater inclusivity, including potential amendments to MiCA that accommodate ethical finance models and support Islamic digital financial innovation. The findings contribute to the global discourse on harmonizing digital asset regulation with diverse ethical and religious frameworks, offering valuable insights for regulators, scholars, and Islamic financial institutions
Non-Fungible Tokens (NFTs) offer a promising mechanism to protect Australian and Indigenous artists' copyright. They represent and transfer the value of artwork in digital form. Before adopting NFTs to protect Australian artwork, we in this paper investigate them empericially. We focus on examining the details of NFT structure. We start from the underlying structure of NFTs to show how they represent copyright for both artists and production owners, as well as how they aim to safeguard or secure the value of digital artworks. We then involve data collection from various types of sources with different storage methods, including on-chain, centralized, and decentralized systems. Based on both metadata and artwork content, we present our analysis and discussion on the following key issues: copyright, security and artist identification. The final results of the evaluation, unfortnately, show that the NFT is NOT ready to protect Australian and Indigenous artists' copyright.
Victoria L. Lemieux, Rosa Gil, Faith Molosiwa, Qizheng Zhou · 8 authors
As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.
Rugsafe introduces a comprehensive protocol aimed at mitigating the risks of rug pulls in the cryptocurrency ecosystem. By utilizing cryptographic security measures and economic incentives, the protocol provides a secure multichain system for recovering assets and transforming rugged tokens into opportunities and rewards. Foundational to Rugsafe are specialized vaults where rugged tokens can be securely deposited, and anticoin tokens are issued as receipts. These anticoins are designed to be inversely pegged to the price movement of the underlying rugged token. Users can utilize these anticoins within the ecosystem or choose to burn them, further securing the protocol and earning additional rewards. The supply of the native Rugsafe token is dynamically adjusted based on the volume, value, and activity of rugged tokens, ensuring stability and resilience. By depositing rugged tokens into a vault on several chains, and by burning anticoins, users receive incentives on the RugSafe chain. This protocol's vaults are designed to work in heterogenous blockchain ecosystems, offering a practical and effective solution to one of the most significant challenges in the cryptocurrency market.
Blockchain bridges have become essential infrastructure for enabling interoperability across different blockchain networks, with more than $24B monthly bridge transaction volume. However, their growing adoption has been accompanied by a disproportionate rise in security breaches, making them the single largest source of financial loss in Web3. For cross-chain ecosystems to be robust and sustainable, it is essential to understand and address these vulnerabilities. In this study, we present a comprehensive systematization of blockchain bridge design and security. We define three bridge security priors, formalize the architectural structure of 13 prominent bridges, and identify 23 attack vectors grounded in real-world blockchain exploits. Using this foundation, we evaluate 43 representative attack scenarios and introduce a layered threat model that captures security failures across source chain, off-chain, and destination chain components. Our analysis at the static code and transaction network levels reveals recurring design flaws, particularly in access control, validator trust assumptions, and verification logic, and identifies key patterns in adversarial behavior based on transaction-level traces. To support future development, we propose a decision framework for bridge architecture design, along with defense mechanisms such as layered validation and circuit breakers. This work provides a data-driven foundation for evaluating bridge security and lays the groundwork for standardizing resilient cross-chain infrastructure.