The digitalization of the architecture, engineering, and construction (AEC) industry has demonstrated the revolutionary potential of integrating blockchain technology with building information modelling (BIM). However, the selection of the most appropriate blockchain solution is a multiple-criteria decision-making (MCDM) problem, which is usually influenced by conflicting criteria and deep uncertainty. To overcome this, the present study proposes an extended Fermatean fuzzy weighted aggregated sum product assessment (Extended FF-WASPAS) model. Unlike existing Fermatean fuzzy WASPAS (FF-WASPAS) methods, which are based on a single expert and may be biased, the proposed model incorporates the evaluations of multiple decision makers (DMKs) through a consensus-driven mechanism to ensure unbiased and accurate results. A case study is conducted to evaluate five leading blockchain platforms, Hyperledger Fabric, Polkadot, Tezos, Ethereum, and Algorand, under eight BIM-related criteria. The result indicates that Ethereum is the best blockchain platform to digitalize BIM compared to the other platforms because it is scalable, interoperable, secure, and has a wide range of applications in the real world. Sensitivity analysis over a wide range of parameter values, as well as DMKs assigned with different weight sets, confirmed the stability of the ranking. Furthermore, a quantitative comparative analysis with FF multiple criteria group decision-making (FF-MCGDM) and FF-WASPAS approaches, as well as a qualitative analysis with existing models in various fuzzy environments, confirms the robustness and reliability. Overall, the study provides a strong, interpretable, and consensus-based decision-support framework with high practical value for AEC stakeholders who wish to deploy secure, transparent, and efficient blockchain-enabled BIM solutions.
Background The medical device sector, valued at $569 billion, faces persistent financing challenges. Around 78% of startups fail because of capital shortages, not due to lacking technical quality. Blockchain-based tokenization emerges as a way to broaden access, yet success relies on economic factors of platforms and clear regulations. Methods Transaction cost data from Bitcoin, Ethereum, and XRP Ledger covered 540 days from January 2024 to June 2025, providing 3,240 observations per network. Experts, numbering 12, participated in a modified Delphi method to form a framework tailored to healthcare. Project outcomes came from Monte Carlo simulations running 10,000 iterations, checked by a triple control-loop system, and compared against two real-world examples. Volumes of transactions drew from stochastic models involving monthly, quarterly, and annual elements, mixing fixed regulatory needs with variable market influences. Results Layer-1 (L1) fees differ by orders of magnitude; representative 2025 snapshots show BTC and ETH L1 far above XRPL and major ETH L2s. XRPL fees are typically a tiny fraction of a cent; the base cost is 10 drops (0.00001 XRP) and is dynamically adjusted by network load. Probabilities of success varied from 10.1% to 12.3% on Bitcoin, 31.4%–48.3% on Ethereum based on Layer-2 adoption, and 71.6%–73.2% on XRP Ledger. Investor involvement correlated negatively with logarithms of costs, showing Spearman <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow></mml:math> of −0.91. Differences in success exceeded 60 percentage points across platforms. Examples illustrated how elevated expenses reduce engagement in VitaDAO on Ethereum, whereas low-cost systems like XRP Healthcare support ongoing involvement. Conclusion Choosing a blockchain platform critically influences viability in tokenizing medical devices. Layer-2 options reduce cost gaps but add complexities in bridging and use. Platforms offering stability, minimal fees, and regulatory alignment promote wider inclusion and reliable funding. Technical features, steady costs, and readiness for compliance together shape whether tokenization boosts innovation in healthcare or maintains barriers.
Open access
Quality and Safety in Healthcare
Neuroethics, Human Enhancement, Biomedical Innovations
Slow and disputed progress payments undermine contractor liquidity and project schedules due to manual verification, fragmented data, and limited transparency. This paper presents a prototype system that incrementally integrates digital twin (DT), building information modeling (BIM), and blockchain to automate milestone-based payments. The continuously updated DT is conceptualized as a dynamic oracle, capturing real-time site conditions and comparing them with structured BIM milestones. Verified achievements trigger Ethereum smart contracts referencing Merkle-proofed evidence stored off-chain in InterPlanetary File System (IPFS), with disbursements authorized via Gnosis Safe multi-signature wallets. A prototype on a police station project shortened verification to payment from several days to minutes and eliminated disputes across all milestones. A survey of industry professionals confirmed gains in efficiency, transparency, and trust. The proposed system provides a practical foundation for transparent, automated payments and offers pathways for future adoption such as stablecoin settlement and public sector integration. • Digital twins resolve blockchain oracle challenges in construction payments. • BIM-blockchain integration reduces payment verification from days to minutes. • Smart contracts with multi-signature governance secure transaction integrity. • Stakeholders confirm enhanced payment speed, transparency, and trust.
Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully designed and implemented. This paper investigates the capability of large language models (LLMs) to detect OWASP-inspired vulnerabilities in smart contracts beyond the Ethereum Virtual Machine (EVM) ecosystem, focusing specifically on Solana and Algorand. Given the lack of labeled datasets for non-EVM platforms, we design a synthetic dataset of annotated smart contract snippets in Rust (for Solana) and PyTeal (for Algorand), structured around a vulnerability taxonomy derived from OWASP. We evaluate LLMs under three configurations: prompt engineering, fine-tuning, and a hybrid of both, comparing their performance on different vulnerability categories. Experimental results show that prompt engineering achieves general robustness, while fine-tuning improves precision and recall on less semantically rich languages such as TEAL. Additionally, we analyze how the architectural differences of Solana and Algorand influence the manifestation and detectability of vulnerabilities, offering platform-specific mappings that highlight limitations in existing security tooling. Our findings suggest that LLM-based approaches are viable for static vulnerability detection in smart contracts, provided domain-specific data and categorization are integrated into training pipelines.
This study employs a theoretical, system design–based methodology to propose the Palm GreenChain framework—a blockchain-based platform aimed at enhancing traceability, transparency, financial coverage, and accountability in green bond financing for sustainable palm oil production in Malaysia. The methodology integrates Ethereum-compatible smart contracts, ESG oracles, IPFS-based data storage, and DAO (Decentralized Autonomous Organization) governance to structure a digital green bond lifecycle. Rather than relying on empirical data collection, the framework is conceptualized through the development of a multi-layered blockchain architecture and validated via comparative analysis with analogous blockchain applications in agriculture. The proposed system is designed to enable real-time traceability of green bond disbursements, automate ESG compliance verification using satellite and IoT data, and strengthen accountability and access to climate finance for smallholder farmers. By embedding performance-based returns within smart contracts, the model aligns financial incentives with conservation goals. Leveraging Malaysia’s advanced land administration infrastructure and digital capabilities, the framework presents a scalable, open-source solution to reduce greenwashing, expand financial inclusion in underserved agricultural communities, and enhance transparency and investor confidence in sustainable agricultural finance. By directly linking green finance to verifiable sustainability outcomes, Palm GreenChain addresses key limitations in conventional green bond mechanisms. Its applicability across diverse agricultural sectors positions it as a replicable blueprint for broader sustainable development. The framework is openly available via its GitHub repository.
Cryptocurrency investment is a rapidly growing financial sector, marked by high volatility, decentralized technologies, and significant profit potential. Investors use strategies like long-term holding (“HODLing”), portfolio diversification, and short-term trading. “HODLing” relies on long-term value appreciation but requires resilience to price fluctuations. Diversifying with assets like Bitcoin and Ethereum reduces risk due to their low correlation with traditional investments. The crypto market is highly sensitive to geopolitical, economic, and technological factors, attracting investors during economic instability. Advanced models like LASSO and AutoEncoder aid in price prediction and strategy optimization. Despite high return potential, careful risk management is essential due to volatility and regulatory uncertainty. This study experimentally applies identical cryptocurrency portfolios to different investment strategies, identifying the most profitable approach.
Jonas Gebele, Timm Mutzel, Burak Oez, Florian Matthes
Sealed-bid auctions ensure fair competition and efficient allocation but are often deployed on centralized infrastructure, enabling opaque manipulation. Public blockchains eliminate central control, yet their inherent transparency conflicts with the confidentiality required for sealed bidding. Prior attempts struggle to reconcile privacy, verifiability, and scalability without relying on trusted intermediaries, multi-round protocols, or expensive cryptography. We present a sealed-bid auction protocol that executes sensitive bidding logic on a Trusted Execution Environment (TEE)-backed confidential compute blockchain while retaining settlement and enforcement on a public chain. Bidders commit funds to enclave-generated escrow addresses, ensuring confidentiality and binding commitments. After the deadline, any party can trigger resolution: the confidential blockchain determines the winner through verifiable off-chain computation and issues signed settlement transactions for execution on the public chain. Our design provides security, privacy, and scalability without trusted third parties or protocol modifications. We implement it on SUAVE with Ethereum settlement, evaluate its scalability and trust assumptions, and demonstrate deployment with minimal integration on existing infrastructure.
Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies, may improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.
El estudio analizó el comportamiento de las criptomonedas Bitcoin y Ethereum durante el año 2024 mediante la construcción de un modelo estadístico ARIMA (AutoRegressive Integrated Moving Average). La investigación utilizó un enfoque cuantitativo que se dividió en cuatro etapas: recopilación y limpieza de datos históricos, verificación de la estabilidad de los datos, identificación y estimación de los mejores parámetros usando criterios de información, y validación mediante medidas de precisión y análisis de residuos. Los resultados demostraron que el modelo ARIMA fue útil en el pronóstico de valores en mercados estables, destacando su trayectoria en el análisis de datos financieros. Además, los valores bajos de RMSE y MAPE validaron que el modelo tiene la capacidad de realizar pronósticos precisos en escenarios con alta frecuencia. En particular, el MAPE de Bitcoin fue 2,25 % y el de Ethereum 2,85 % durante la etapa de prueba, demostrando que los valores pronosticados tuvieron una ligera desviación con respecto a los reales. No obstante, el modelo puede verse afectado en periodos de alta volatilidad, como en las burbujas especulativas o en los desplomes bursátiles, ya que no tiene la capacidad de adaptarse dinámicamente a cambios súbitos en los parámetros; sin embargo, su utilidad puede mejorar al combinar modelos híbridos con ARIMA.
Xihan Xiong, Zhipeng Wang, Qin Wang, William Knottenbelt
Decentralized communication is becoming an important use case within Web3. On Ethereum, users can repurpose the transaction input data field to embed natural-language messages, commonly known as Input Data Messages (IDMs). However, as IDMs gain wider adoption, there has been a growing volume of toxic content on-chain. This trend is concerning, as Ethereum provides no protocol-level support for content moderation. We propose two moderation frameworks for Ethereum IDMs: (i) BUILDERMOD, where builders perform semantic checks during block construction; and (ii) USERMOD, where users proactively obtain moderation proofs from external classifiers and embed them in transactions. Our evaluation reveals that BUILDERMOD incurs high block-time overhead, which limits its practicality. In contrast, USERMOD enables lower-latency validation and scales more effectively, making it a more practical approach in moderation-aware Ethereum environments. Our study lays the groundwork for protocol-level content governance in decentralized systems, and we hope it contributes to the development of a decentralized communication environment that is safe, trustworthy, and socially responsible.
Dini Rohmayani, Ilham Alfath, Castaka Agus Sugianto
Abstrak - Implementasi Non-Fungible Token (NFT) sering menghadapi masalah permanensi data karena ketergantungan pada penyimpanan terpusat yang rentan terhadap penghapusan dan perubahan. Penelitian ini mengembangkan aplikasi web berbasis Decentralized Application (DApp) untuk pembuatan Non-Fungible Token (NFT) yang mengintegrasikan InterPlanetary File System (IPFS) dengan Smart Contract ERC-721 pada blockchain Ethereum. Metode pengembangan menggunakan pendekatan waterfall dengan implementasi React.js dan Next.js untuk frontend serta Solidity untuk smart contract. Sistem dirancang dengan arsitektur three-tier yang memfasilitasi pembuatan koleksi Non-Fungible Token (NFT) melalui kontrak Factory dan pengelolaan token melalui NFTCollection, dengan metadata dan aset digital disimpan menggunakan Content Identifier (CID) pada InterPlanetary File System (IPFS). Pengujian blackbox menunjukkan seluruh fungsi sistem berjalan sesuai spesifikasi, sementara User Acceptance Testing (UAT) dengan 24 responden menghasilkan tingkat penerimaan 90%. Hasil penelitian membuktikan bahwa integrasi InterPlanetary File System (IPFS) dengan smart contract ERC-721 dapat mengatasi permasalahan permanensi metadata dan aset digital, sekaligus menyederhanakan proses minting Non-Fungible Token (NFT) bagi pengguna non-teknis melalui antarmuka yang intuitif.Kata kunci: Non-Fungible Token (NFT); ERC-721; IPFS; Kontrak Pintar; Blockchain; Abstract - Podo Practical implementations of Non-Fungible Tokens (NFTs) often face data permanence issues due to reliance on centralized storage systems vulnerable to deletion and modification. This research develops a web-based Decentralized Application (DApp) for NFT creation that integrates the InterPlanetary File System (IPFS) with ERC-721 Smart Contracts on the Ethereum blockchain. The development methodology employs a waterfall approach with React.js and Next.js for frontend implementation and Solidity for smart contracts. The system is designed with a three-tier architecture facilitating NFT collection creation through the NFTFactory contract and token management through NFTCollection, with metadata and digital assets stored using Content Identifiers (CID) on IPFS. Black box testing demonstrates that all system functions operate according to specifications, while User Acceptance Testing (UAT) with 24 respondents yields an acceptance rate of 90%. The research findings prove that integrating IPFS with ERC-721 smart contracts addresses metadata and digital asset permanence issues while simplifying the NFT minting process for non-technical users through an intuitive interface.Keywords: Non-Fungible Token (NFT); ERC-721; IPFS; Smart Contract; Blockchain;
Raffaele Cristodaro, Benjamin Kraner, Claudio J. Tessone
This paper investigates the impact of sanctions on Tornado Cash, a smart contract protocol designed to enhance transaction privacy. Following the U.S. Department of the Treasury's sanctions against Tornado Cash in August 2022, platform activity declined sharply. We document a significant and sustained reduction in transaction volume, user diversity, and overall protocol utilization after the sanctions were imposed. Our analysis draws on transaction data from three major blockchains: Ethereum, BNB Smart Chain, and Polygon. We further examine developments following the partial lifting and eventual removal of sanctions by the U.S. Office of Foreign Assets Control (OFAC) in March 2025. Although activity partially recovered, the rebound remained limited. The Tornado Cash case illustrates how regulatory interventions can affect decentralized protocols, while also highlighting the challenges of fully enforcing such measures in decentralized environments.
Raffaele Cristodaro, Benjamin Kraner, Claudio J. Tessone
Tornado Cash is a decentralised mixer that uses cryptographic techniques to sever the on-chain trail between depositors and withdrawers. In practice, however, its anonymity can be undermined by user behaviour and operational quirks. We conduct the first cross-chain empirical study of Tornado Cash activity on Ethereum, BNB Smart Chain, and Polygon, introducing three clustering heuristics-(i) address-reuse, (ii) transactional-linkage, and (iii) a novel first-in-first-out (FIFO) temporal-matching rule. Together, these heuristics reconnect deposits to withdrawals and deanonymise a substantial share of recipients. Our analysis shows that 5.1 - 12.6% of withdrawals can already be traced to their originating deposits through address reuse and transactional linkage heuristics. Adding our novel First-In-First-Out (FIFO) temporal-matching heuristic lifts the linkage rate by a further 15 - 22 percentage points. Statistical tests confirm that these FIFO matches are highly unlikely to occur by chance. Comparable leakage across Ethereum, BNB Smart Chain, and Polygon indicates chain-agnostic user misbehaviour, rather than chain-specific protocol flaws. These results expose how quickly cryptographic guarantees can unravel in everyday use, underscoring the need for both disciplined user behaviour and privacy-aware protocol design. In total, our heuristics link over $2.3 billion in Tornado Cash withdrawals to identifiable deposits, exposing significant cracks in practical anonymity.
Alison Gonçalves Schemitt, Henrique Fan da Silva, Roben Castagna Lunardi, Diego Kreutz · 6 authors
The advent of quantum computing poses a threat to the security of traditional encryption algorithms. This has motivated the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain systems fundamentally rely on cryptographic primitives to guarantee data integrity and transaction authenticity. However, widely used algorithms such as ECDSA, employed in Bitcoin, Ethereum, and other networks, are vulnerable to quantum attacks. Although adopting PQC is essential for long-term security, its computational overhead in blockchain environments remains largely unexplored. In this work, we propose a methodology for benchmarking both PQC and traditional cryptographic algorithms in blockchain contexts. We measure signature generation and verification times across diverse computational environments and simulate their impact at scale. Our evaluation focuses on PQC digital signature schemes (ML-DSA, Dilithium, Falcon, Mayo, SLH-DSA, SPHINCS+, and Cross) across security levels 1 to 5, comparing them to ECDSA, the current standard in Bitcoin and Ethereum. Our results indicate that PQC algorithms introduce only minor performance overhead at security level 1, while in some scenarios they significantly outperform ECDSA at higher security levels. For instance, ML-DSA achieves a verification time of 0.14 ms on an ARM-based laptop at level 5, compared to 0.88 ms for ECDSA. We also provide an open-source implementation to ensure reproducibility and to encourage further research.
As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal · 6 authors
This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1–4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4–10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.
Tradicionalne metode preverjanja prisotnosti, kot so ročno beleženje ali QR kode, so podvržene manipulaciji in ne zagotavljajo zadostne varnosti ter zasebnosti uporabnikov. Magistrsko delo naslavlja te izzive z razvojem decentraliziranega sistema za preverjanje fizične prisotnosti, ki temelji na tehnologiji verige blokov in ničelno spoznavnih dokazih (zk-SNARK). Sistem integrira ZoKrates ogrodje za generiranje zasebnih dokazov, geolokacijsko verifikacijo z GPS koordinatami, Ethereum pametne pogodbe ter hibridni pristop k shranjevanju podatkov. Implementirani so bili večplatformski uporabniški vmesniki (spletna in mobilna aplikacija) z različnimi načini potrjevanja prisotnosti. Razvita rešitev predstavlja funkcionalen in robusten sistem, ki omogoča varno ter transparentno preverjanje prisotnosti brez razkrivanja osebnih podatkov uporabnikov.
This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investors’ reactions to information arrival in a “primary market.” Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.
Vladyslav Nekriach, Sidi Mohamed Beillahi, C. Li, Peilun Li · 7 authors
This paper introduces HEMVM, an innovative heterogeneous blockchain framework that seamlessly integrates diverse virtual machines (VMs), including the Ethereum Virtual Machine (EVM) and the Move Virtual Machine (MoveVM), into a unified system. This integration facilitates interoperability while retaining compatibility with existing Ethereum and Move toolchains by preserving high-level language constructs. HEMVM's unique cross-VM operations allow users to interact with contracts across various VMs using any wallet software, effectively resolving the fragmentation in user experience caused by differing VM designs. Our experimental results demonstrate that HEMVM is both fast and efficient, incurring minimal overhead (less than 4.4 %) for intra-VM transactions and achieving up to 9300 TPS for cross-VM transactions. Our results also show that the cross-VM operations in HEMVM are sufficiently expressive to support complex decentralized finance interactions across multiple VMs. Finally, the parallelized prototype of HEMVM shows performance improvements up to 44.8 % compared to the sequential version of HEMVM under workloads with mixed transaction types.
W. M. A. B. Wijesundara, Joong-Sun Lee, Eleni Aloupogianni, Dara Tith · 6 authors
Rapid proliferation of smart home IoT devices has intensified the demand for secure, scalable, and autonomous firmware authentication mechanisms. Traditional centralized solutions face challenges related to privacy concerns, limited scalability, and vulnerability to single point of failure. In this paper, we propose DIDAuth-IoTFW, a novel decentralized identity and firmware authentication framework that uniquely integrates Ethereum Layer-2 Arbitrum, InterPlanetary File System (IPFS), and W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). DIDAuth-IoTFW provides a complete firmware authentication life cycle, from decentralized identity registration to real-time, on-chain verifiable revocation. While enabling autonomous, cryptographic verification directly on resource-constrained IoT devices and ensuring reliable performance even when gateways are compromised or unavailable. Our proof-of-concept implementation on ESP32 and Raspberry Pi achieved complete resistance to replay, forgery, and revocation threats with verification consistently under 1.2 s. Compared to prior work, DIDAuth-IoTFW uniquely combines firmware–VC hash binding, contract binding that prevents cross-registry replay, and device-side enforcement resilient to gateway compromise. Experimental results indicate a robust, privacy-preserving, and scalable alternative to centralized firmware-update pipelines for smart-home IoT.
Blockchain technologies have profoundly transformed information systems by providing decentralized infrastructures that enhance transparency, security, and traceability. Ethereum, in particular, supports smart contracts and facilitates the development of decentralized finance (DeFi), non-fungible tokens (NFTs), and Web3 applications. However, its openness also enables illicit activities, including fraud and money laundering, through anonymous wallets. Identifying wallets involved in large transfers or abnormal transactional patterns is therefore critical to ecosystem security. This study proposes an AI-based framework employing XGBoost, LightGBM, and CatBoost to detect suspicious Ethereum wallets, achieving test accuracies between 95.83% and 96.46%. The system provides near real-time predictions for individual or recent wallet addresses using a pre-trained XGBoost model. To improve interpretability, SHAP (SHapley Additive exPlanations) visualizations are integrated, highlighting the contribution of each feature. The results demonstrate the effectiveness of AI-driven methods in monitoring and securing Ethereum transactions against fraudulent activities.
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive perspective, and combining the two problem reductions enables significant improvements in dynamic programming algorithms such as value iteration. In the case of the LRA which size is parameterized by $κ$ (in Ethereum's case $κ$= 325), we reduce the computational complexity from $O(2^κκ^{2^{κ+2}})$ to $O(κ^4)$ (per iteration). This solution also provide the usual benefits from Dynamic Programming solutions: exponentially fast convergence toward the optimal solution is guaranteed. The dual perspective also simplifies policy extraction, making the approach well-suited for resource-constrained agents who can operate with very limited memory and computation once the problem has been solved. Furthermore, we generalize those results to a broader class of MDPs, enhancing their applicability. The framework is validated through two case studies: a fictional card game and the LRA on the Ethereum random seed consensus protocol. These applications demonstrate the framework's ability to solve large-scale problems effectively while offering actionable insights into optimal strategies. This work advances the study of MDPs and contributes to understanding security vulnerabilities in blockchain systems.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.