Abstract This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
P. Bhuvaneshwari, A Krishnaveni, Harold Robinson, E. Golden Julie
The deep learning technique has emerged as an exemplary model for managing the Artificial Intelligence-based Blockchain framework with technological enhancements to guarantee reliable data through the consensus procedure. The deep learning-enabled blockchain transaction model has involved the development of security to solve the problems of confidentiality and data anonymity. The Hybrid techniques of the Blockchain with the Deep Learning technique are proposed to generate enhanced data durability and its propagation through the enhanced convolutional temporal network (EnCTN) for transaction analysis in a blockchain-enabled Auto Encoder technique. The sliding window extraction technique is used to extract information from a particular window size to evaluate the needed input values from the temporal series. The dilated Convolution is used to capture the long-range dependencies. The proposed technique is implemented in the Ethereum environment using Python, and experimental results show that it has produced an improved performance than the relevant technique in several performance parameters. The anomaly classification accuracy is improved than the relevant technique and it is evaluated using the NSL-KDD dataset. The proposed framework delivers an efficient solution for the real-world anomaly detection application while accurate discovery of temporal anomalies and computational efficiency is enhanced.
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.
Decentralized Finance (DeFi) has emerged as a transformative force in global finance, offering trustless, blockchain-based alternatives to traditional intermediated systems. This paper examines how DeFi innovations — such as tokenized assets, decentralized exchanges (DEXs), and automated smart contracts — are reshaping corporate fundraising. It analyzes the efficiency, accessibility, and regulatory implications of using decentralized protocols for capital raising, comparing DeFi mechanisms (e.g., IDOs, security token offerings, DAOs) with traditional equity and debt issuance models. Using case studies and data from leading DeFi ecosystems (Ethereum, Polygon, Solana) and corporate blockchain pilots, we evaluate DeFi’s impact on fundraising costs, investor reach, and transparency. The findings suggest that while DeFi offers reduced friction and democratized access to capital, challenges in regulation, governance, and investor protection must be resolved before large-scale corporate adoption.
Hessah A. Alsalamah, Saeed Alqahtani, Ghazlan Al-Arifi, Jana Al-Sadhan · 8 authors
Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems.
Froylan Cortés-Santacruz, Luis Antonio Carrillo-Martínez, Luciano García‐Bañuelos, Jesús Anselmo Fortoul-Diaz
Although distributed ledger technologies (DLTs) have transformed financial sectors, their manufacturing applications lack systematic maturity assessment frameworks. Previous reviews identified DLT benefits but show critical gaps, including a lack of quantitative maturity metrics, insufficient categorization of use cases, and limited platform-specific comparative analysis. This systematic literature review addresses these gaps through three key contributions: (i) a novel Distributed Ledger Technology Maturity Level (DLTML) framework, (ii) a four-category manufacturing taxonomy, and (iii) platform-specific implementation analysis. Analyzing 60 primary studies (2018–2025) using Kitchenham’s guidelines, Wohlin’s snowballing, and Treiblmaier’s assessment framework, we answer the following: (i) What are the categories of use of DLT in manufacturing? (ii) What features of DLT are key enablers and what specific challenges have been addressed in current manufacturing solutions? (iii) What is the level of technological maturity of DLT applications in manufacturing according to the existing literature? The category Process Execution Tracking dominates (80 % of the studies), followed by Provenance (65 %), Ownership Management (30 %) and Payment Management (25 %). Smart contracts are the main enablers (81. 67 %), followed by decentralization (48.33 %). Governance mechanisms remain unaddressed, and interoperability progress is limited. Hyperledger Fabric leads privacy-sensitive scenarios (55 %), while Ethereum dominates transparency-focused applications (38 %). DLTML assessment shows that 61.66 % achieve intermediate maturity (DLTML-3), 20 % achieve high-fidelity prototypes (DLTML-4), but none achieves verified operational deployment (DLTML-5). This study provides evidence-based guidance for researchers and decision makers pursuing the adoption of DLT in manufacturing.
NFT (Non-Fungible Token) has emerged as a trending topic in the digital world. This article focuses on the working principle of NFTs and their practical applications in real-world scenarios. Ethereum blockchain serves as the foundational technology that powers NFTs. This document provides a comprehensive technical overview of Ethereum blockchain technology applied in the textile industry for maintaining product ownership verification and authenticity
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Anton Ivashkevich, Matija Piškorec, Claudio J. Tessone
We describe a prototype of a fully capable Ethereum Proof-of-Work (PoW) blockchain network running on multiple Raspberry Pi (RPi) computers. The prototype is easy to set up and is intended to function as a completely standalone system, using a local WiFi router for connectivity. It features LCD screens for visualization of the local state of blockchain ledgers on each RPi, making it ideal for educational purposes and to demonstrate fundamental blockchain concepts to a wide audience. For example, a functioning PoW consensus is easily visible from the LCD screens, as well as consensus degradation which might arise from various factors, including peer-to-peer topology and communication latency - all parameters which can be configured from the central web-based interface.
The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.
Smart contracts enable programmatic agreements but face two persistent problems: high on-chain cost (throughput/latency) and weak privacy (public ledger exposes transaction semantics). We propose a hybrid on-chain/off-chain commitment scheme (HOC-C) that combines lightweight on-chain commitments, verifiable off-chain computation, and succinct zero-knowledge proofs to deliver privacy-preserving contract execution at scale. In HOC-C, sensitive inputs and heavy computations are executed off-chain by a consortium of replicated verifiers; the verifiers publish a succinct zk-SNARK proof of correct execution plus a small state commitment on-chain. The on-chain contract verifies the proof and updates state atomically. To prevent malicious collusion among verifiers, HOC-C integrates an economic incentive layer and challenge windows where anyone can publish refutation proofs; the refutation burden is designed to be less than the honest-verifier cost. We implement HOC-C using a prototype that plugs into an EVM-compatible chain (Ethereum testnet) and evaluate performance for representative workloads (private auctions, confidential supply-chain workflows, private token-transfer batching). The system reduces gas cost by an order of magnitude compared to naive on-chain execution while preserving end-to-end confidentiality for user inputs. We analyze security properties (soundness, liveness, and economic incentive compatibility) and discuss trade-offs: proof generation latency vs. throughput, verifier decentralization vs. amortized cost. HOC-C offers a practical roadmap for adopting private, inexpensive smart contracts on mainstream blockchains.
The global Blockchain Market, valued at USD 24.20 billion in 2024, is projected to reach USD 301.02 billion by 2030, expanding at an impressive CAGR of 60.2% from 2025 to 2030. The increasing digital payment transactions, rising demand for security, and rapid adoption of cryptocurrencies are fueling market growth. However, challenges such as regulatory uncertainty and high implementation costs remain significant barriers. Despite these limitations, the integration of decentralized finance (DeFi), AI, and advanced technological frameworks presents substantial opportunities for innovation. Leading industry players, including IBM, Ethereum, Hyperledger, and Oracle, are actively engaging in partnerships and technological advancements to strengthen their market presence. As blockchain technology continues to evolve, its applications are expanding across sectors including BFSI, healthcare, government, logistics, and retail. This manuscript provides an in-depth analysis of drivers, restraints, opportunities, segmentation, regional insights, and competitive landscape shaping the future of the global blockchain economy.
T J E N N I N G, Kalokhe Omkar Nanabhau, Takale Ram Arjun, Borge Akash Sandip
The proliferation of digital documents and academic credentials in today's interconnected world has created both opportunities and vulnerabilities. Traditional certificate issuance and storage mechanisms are highly susceptible to forgery, duplication, and unauthorized manipulation, undermining the trustworthiness of academic and professional qualifications. To address these challenges, this research proposes a blockchain-based certificate generation and verification system that ensures transparency, immutability, and trust across stakeholders. Leveraging distributed ledger technology, the system securely records certificate metadata and unique identifiers, enabling real-time, tamper-proof validation without reliance on intermediaries. The architecture integrates modern web technologies such as Next.js for frontend and backend services, MongoDB for scalable storage, JWT for authentication, and cryptographic techniques including bcrypt for enhanced security. Additionally, smart contracts deployed on Ethereum/Ganache enable decentralized storage and validation, while certificate data is simultaneously linked with non-fungible tokens (NFTs) to provide verifiable ownership and authenticity. This integration not only eliminates certificate fraud but also facilitates seamless verification across institutions, employers, and regulatory authorities. By combining blockchain's decentralized security with user-friendly web applications, the proposed approach aims to create a globally interoperable, cost-effective, and future-ready framework for academic and professional certification systems.
Blockchain is one of the most consequential innovations since the world wide web. Although blockchain is argued to remove, displace, or redistribute expertise, there is little understanding of the role of expertise in blockchain ecosystems, and more generally the expertise that fuels the development of new technologies by means of open, fluid, and heterogeneous knowledge contributions. An empirical study of the social organization of the Ethereum community, the second largest blockchain ecosystem after Bitcoin, reveals the contrasting tensions involved in setting up a system of decentralized expertise. The alternate community mantras “rough consensus, running code” and “wide consensus, better code?” suggest that the Ethereum community enacts expertise centralization and decentralization practices simultaneously to create a fragile balance between individualized accountabilities and a generalized sense of diffused participation. These practices unfold along a continuum of routine operations punctuated by critical events and are both essential for navigating the uncertainties of decentralized organizations. The study contributes to research on new forms of expertise occasioned by emerging technologies, and in particular to our understanding of blockchain expertise. The study’s relational perspective on expertise adds to research on the dynamics of knowledge de/centralization in online communities.
Matei Sofronie, Andrei Brînzea, Alexandru Bratu, Iulian Aciobăniţei · 5 authors
Verifiable Credentials (VCs) are a core component of decentralized identity systems, enabling individuals to prove claims without centralized intermediaries. However, managing VC revocation across segregated blockchain networks remains a key interoperability challenge. In this paper, we present a bidirectional blockchain bridge that enables the cross-chain verification of VCs between two Ethereum-compatible private blockchain networks: Geth and Besu. The system allows credentials issued and revoked on one chain to be validated from another without duplicating infrastructure or compromising security. Our architecture combines on-chain smart contracts with an off-chain relay, ensuring auditable, low-latency credential checks across chains. Our proposal is validated through an open-source working prototype. It is particularly relevant for domains where independent organizations must validate shared credentials across segregated blockchain infrastructures, including education, healthcare, and governmental identity services.
This paper investigates the role of artificial intelligence (AI) stocks and AI cryptocurrencies in portfolio diversification, reflecting on the rising interest in technology-oriented assets. While much research has focused on the diversification, hedging, and safe-haven properties of digital assets, such as Bitcoin and Ethereum, this study focuses on whether AI cryptocurrencies and AI stocks provide untapped diversification potential. Using mean-variance, risk parity, and higher-order moments approaches, we construct portfolios that combine AI stocks, AI cryptocurrencies, and traditional assets under various optimization frameworks. The findings reveal that the mean-variance framework is more conservative in allocating to AI cryptocurrencies, while the higher-order moments approach accommodates for greater flexibility. Seemingly, investors may benefit from expanding their asset pool to incorporate AI stocks and AI cryptocurrencies. Across most portfolio settings, gold and commodities dominate allocations, followed by AI stocks, with AI cryptocurrencies receiving only marginal weights owing to their high volatility. However, allocations to AI cryptocurrencies increase as investor risk tolerance increases, thereby highlighting their potential for risk-seeking portfolios. Overall, the results indicate that AI stocks and AI cryptocurrencies can enhance portfolio diversification and improve risk-return outcomes. These results offer valuable insights for investors seeking to optimize their portfolios, through exposure to emerging technology-driven assets while balancing traditional risk considerations. • The study explores the diversification potential of AI Stocks and AI Cryptocurrencies to a traditional portfolio. • Dominated by NVIDIA and Tesla, AI stocks perform better than AI cryptocurrencies. • AI cryptocurrencies offer limited diversification benefits while significantly increasing portfolio risk. • Unlike AI stocks, AI cryptocurrencies are not dominated by a single player in portfolio diversification. • Allocation to AI cryptocurrencies is highly sensitive to investor risk aversion, particularly driven by their explosive market behaviour.
Farhad Asgarov, Fatih Said Duran, Namig Samadov, Şerif Bahtıyar
Cite this article as: F. Asgarov, F. S. Duran, N. Samadov and Ş. Bahtiyar, “Blockchain based ownership and DNS configuration with ethereum rollups,”Electrica, 25, 0051, doi: 10.5152/electrica.2025.25005.
The rise of Decentralized Finance (DeFi), enabled by blockchain technology, has introduced open and transparent financial ecosystems that contrast sharply with traditional financial systems. While DeFi expands the financial landscape and democratizes participation in global markets, it also introduces new complexities. Classic valuation models used in traditional finance often fall short in this context. However, DeFis transparency---where all transactions are publicly recorded---offers a unique opportunity to model and understand market behavior using modern analytical tools. Motivated by the challenges and opportunities of DeFi, the main goal of this thesis is to propose novel methods to understand market dynamics through the lens of network science and machine learning. To this end, we focus on four specific objectives: (i) assess whether structural information from blockchain transaction networks provides predictive signals beyond traditional indicators; (ii) develop robust trust-based valuation metrics for DeFi protocols; (iii) develop a framework for forecasting financial time series through uncertainty-aware machine learning architectures; (iv) construct diversified portfolios using network-based representations of asset relationships. First, using Ethereum as a case study, we analyze the influence of the transaction network on market trends by comparing the performance of two machine learning models: one that uses technical analysis and social media indicators commonly found in the literature and another that incorporates structural properties of the transaction network. We found that by including transaction network information, we can anticipate 46% more uptrends and 19% more downtrends, highlighting the predictive power of the transaction network. Second, we introduce the TVL/MCAP bands as a tool to identify periods of overconfidence and underconfidence in the DeFi market. We show that extreme values of this indicator can signal price movements: values above the 95th percentile are associated with a 15\% higher return in the following month, while values below the 5th percentile anticipate declines, highlighting investor confidence as a key market driver. Third, we address the need for forecasts that not only anticipate market trends but also quantify the uncertainty surrounding them. To this end, we integrate Reservoir Computing (RC) with conformal prediction methods to provide statistically rigorous forecasts along with prediction intervals. We found that RC outperform traditional econometric models, particularly in anticipating the trend of financial time series. Furthermore, we show that conformal methods, especially quantile-conformal variants, significantly improve forecast reliability while adapting to market volatility. Finally, we address the challenge of portfolio optimization using network-based methods. Specifically, we model the network of relationships between cryptocurrencies to obtain a market representation that enables selecting a more diversified portfolio. We find that peripheral assets enhance portfolio stability and returns, while links bridging network communities carry higher risk. Thereby, these results highlight the importance of structural diversification in volatile markets. In addition, we contribute to refining pairs trading strategies by proposing the Hurst exponent to identify rapid mean-reversion opportunities. We show that anti-persistent values of H lead to faster reversion and consistent returns---minimizing trading costs and enabling broader portfolio construction. In conclusion, this thesis provides an interdisciplinary analytical framework that advances our understanding of DeFi markets. By introducing network-based indicators, trust metrics, uncertainty-aware forecasts, and diversification strategies grounded in market structure, we provide new tools for investors and researchers to navigate the complexity and volatility inherent in decentralized financial systems. RESUMEN El auge de las Finanzas Descentralizadas (DeFi), impulsado por la tecnología blockchain, ha dado lugar a ecosistemas financieros más accesibles y transparentes que contrastan con los sistemas financieros tradicionales. DeFi amplía el panorama financiero actual e introduce nuevos retos, como la necesidad de un nuevo modelo de valoración de los activos. No obstante, el hecho de que todas las transacciones son públicas, ofrece una oportunidad única para modelar y comprender la dinámica del mercado mediante nuevas herramientas analíticas. Esta tesis tiene como objetivo principal proponer nuevos métodos para comprender la dinámica del mercado desde la perspectiva de los sistemas complejos y el aprendizaje automático. Para ello, nos centramos en cuatro objetivos específicos: (i) evaluar si la información estructural de las redes de transacciones aporta señales predictivas más allá de los indicadores tradicionales; (ii) desarrollar métricas de valoración de los protocolos DeFi basadas en la confianza de los inversores; (iii) construir un marco metodológico para predecir series temporales financieras mediante arquitecturas de aprendizaje automático que incorporen incertidumbre; (iv) construir portfolios diversificados utilizando representaciones de la red de relaciones entre criptomonedas. En primer lugar, utilizando Ethereum como caso de estudio, analizamos la influencia de la red de transacciones sobre la tendencia del mercado comparando dos modelos de aprendizaje automático: uno que emplea indicadores de análisis técnico y de redes sociales comunes en la literatura, y otro incluyendo propiedades estructurales de la red de transacciones. Los resultados muestran que incluyendo información de la red podemos anticipar un 46% más de tendencias alcistas y un 19% más de tendencias bajistas, lo que subraya el poder predictivo de la red de transacciones. En segundo lugar, introducimos las bandas TVL/MCAP para identificar períodos de sobreconfianza y desconfianza en el mercado DeFi. Demostramos que valores extremos de este indicador anticipan movimientos en el precio: valores por encima del percentil 95 se asocian con un rendimiento 15% superior en el mes siguiente, mientras que valores por debajo del percentil 5 anticipan caídas. En tercer lugar, abordamos la necesidad de predicciones que no solo anticipen tendencias del mercado, sino que también cuantifiquen la incertidumbre. Para ello, integramos Reservoir Computing (RC) con métodos de predicción conforme para generar predicciones estadísticamente rigurosas junto con intervalos de confianza. Mostramos que RC supera a los modelos econométricos tradicionales, especialmente anticipando la tendencia del precio. Además, los métodos conformes ---en particular las variantes de cuantiles--- mejoran significativamente la fiabilidad de las predicciones al adaptarse a la volatilidad del mercado. Por último, abordamos el problema de optimización de portfolios mediante métodos basados en redes. Específicamente, modelamos la red de relaciones entre criptomonedas para seleccionar un portfolio más diversificado. Observamos que evitar pares que conectan distintas comunidades en la red y priorizar activos periféricos aumenta el rendimiento y disminuye el riesgo, demostrando así la importancia de una diversificación estructural. Además, proponemos el uso del exponente de Hurst para identificar oportunidades que revierten antes a la media en estrategias de pairs trading. En conclusión, esta tesis propone un marco analítico interdisciplinar que contribuye al entendimiento de los mercados DeFi. Al introducir indicadores basados en redes, métricas de confianza, predicciones con estimación de incertidumbre y estrategias de diversificación basadas en la estructura del mercado, ofrecemos nuevas herramientas para que inversores e investigadores naveguen la complejidad y volatilidad propias de los sistemas financieros descentralizados.
Mohammad Madine, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob
In recent years, the healthcare sector has been increasingly challenged in securing patient identities and medical records on blockchain due to rising privacy demands and strict regulatory requirements. Although advanced techniques like self-sovereign identity and zero-knowledge proofs (ZKPs) show promise, these solutions fail to limit unwarranted patient data disclosure effectively. In this paper, we propose a ZKP-based solution that combines STARKs and anonymous credentials to enable anonymous authentication and enhance privacy across both public and private blockchains. Leveraging transparent ZKP schemes and anonymous credentials, our approach ensures unlinkability by preventing the correlation of multiple patient interactions. We present sequence diagrams of real-world interactions, detailed algorithms for on- and off-chain computations, and implement the system on Ethereum and Starknet blockchains. We present a rigorous evaluation of the proposed solution, encompassing smart contract testing on Starknet networks, transaction cost analysis, performance benchmarking, scalability assessment, and static security auditing. The results demonstrate consistent and economically viable transaction costs, millisecond-level execution times for credential issuance, presentation generation, and verification, linear scalability with increasing claim count and size. We compare our solution with state-of-the-art ZKP-based identity systems to demonstrate its superiority. We further discuss its broader applicability beyond healthcare, including domains such as finance, education, and supply chain management. We make the smart contract codes publicly available on GitHub.
Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
This poster introduces HarvETHter, a smart contract decompiler for EVM-based platforms such as Ethereum, Binance, and Polygon. We present the corpus completeness hypothesis, which we investigate through HarvETHter. Relying on our hypothesis, HarvETHter sources knowledge of the Ethereum blockchain and leverages it to decompile smart contracts to Solidity source code.
Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.