Blockchain technology has changed forever the concept of decentralized systems, which provide a secure, unchangeable (immutable), and distributed (shared) ledger. However, many organizations are concerned about how secure the foundations of Blockchain are because of exposed Cryptographic Vulnerabilities and Emerging Threats to Cryptography Used in Blockchain Technology. This paper analyzes these two issues. The first issue is an analysis of the different frameworks of existing blockchain technologies that are established on Cryptographic bases. The second issue addresses and categorizes the Security Threats Facing Blockchain Technology Today and into the Future. This research paper discusses Consensus Mechanisms, Encryption Algorithms, and Smart Contracts that are utilized in several of the more well-known Blockchain Technology Frameworks and notes various vulnerabilities associated with them, including Sybil Attacks, Quantum Computing Based Exploits, and Exploits associated with DeFi (Decentralized Finance). This research also found that a significant gap exists today regarding Threats to Blockchain Technology Frameworks and how those Threats may be leveraged to exploit the Vulnerability. Additionally, a Critical Need exists for the use of Proactive Threat Intelligence Processes, as well as Responsible Cryptography Practices, within the Ecosystems of Blockchain Technology.
Keqiu Li, Changzhi Li, Yang Shi, Dengcheng Hu ¡ 7 authors
Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.
Regardless of oneâs opinion, Bitcoinâs presence in the global economy is growing. However, Bitcoinâs emerging role and its implications are greatly under-researched, particularly in the context of government interest in Bitcoin. Nonetheless, increased private investment in Bitcoin and increased government interest in formally incorporating Bitcoin into existing economic systems, suggest a new development within the global economy that must be investigated. Through qualitative text analysis of pro-Bitcoin narratives presented in digital media platforms and official government policies and public statements, this thesis explores how private and government interest in Bitcoin is explained and framed within these contexts. This study finds that there are many important nuances within pro-Bitcoin narratives in the context of private interest that challenge and expand contemporary thinking. It presents new insights into government interest in Bitcoin, particularly concerning its intended role and future, suggesting it will have a presence in efforts beyond finance. Finally, this thesis suggests that despite converging attitudes in private and governmental pro-Bitcoin narratives, diverging attitudes reflect curious implications concerning distrust and dissatisfaction in government efforts. Ultimately, this study reflects that Bitcoin is a dynamic and non-traditional development that requires continuous research to better understand its present and future role in global systems.
Cross-border remittances are important sources of financial support for families, especially in developing countries, facilitating access to basic human necessities like education and healthcare. In spite of their stability through international crises like COVID-19, and India being the largest recipient in 2024, conventional remittance systems based on SWIFT and Correspondent Banking persist with high costs, lagged processing times, and low transparency. This paper suggests a blockchain-based remittance platform that overcomes these limitations by removing intermediaries, facilitating real-time peer-to-peer transfer through automation of smart contracts. The platform makes use of stablecoins to provide volatility resistance. Secure custodial wallets, as well as strict KYC/AML compliance, provide user authentication and regulatory compliance. The designed architecture improves speed, lowers the cost of transactions, and provides regulatory trustâshowing a scalable, secure, and inclusive alternative to traditional remittance systems.
Traditional technical solutions face inherent limitations in ensuring data ownership transparency, tamper-proofing, and traceability in data factor markets, particularly regarding core challenges like unclear ownership rights, high transaction costs, and trust deficits. This paper proposes and designs a blockchain-based data trust registration system. The system is theoretically grounded in the âthree rights separationâ framework of ârights bundlesâ and architecturally divided into six layers: data resource layer, storage layer, core layer, service layer, consensus and security layer, and application layer. It enables systematic processing and hierarchical interaction of complex data through progressive data trust certification, ownership transfer and traceability, and integrity verification. This establishes a collaborative, efficient, and trustworthy data registration infrastructure. By integrating on-chain and off-chain storage coordination, the system achieves efficient certification and secure balance of data rights. Additionally, the paper explores extended technologies like cross-chain interoperability and zero-knowledge proofs to address future needs for multi-chain coexistence and sensitive data registration. Ultimately, this solution aims to provide core technological support for building a trustworthy, efficient, and open data factor circulation environment, thereby reducing transaction trust costs and unlocking the latent economic value of data.
This paper does an in-depth analysis of how blockchain technology and smart contracts instill public confidence in the banking industry. For a very long time now, trust in monetary transactions has been cemented through honest businesses and a regulated intermediary. As it would later turn out, blockchain technology provides an un-hackable, transparent, and decentralized basis upon which trust is built. In this bid to cut the involvement of third parties, this study analyzes to what extent blockchain technology and smart contracts facilitate a shift in trust to programmable contracts. Basically, this essay goes into detail about how blockchain technology and smart contracts can be applied with a view to reconsidering the very concept of trust. This study adopted a mixed-method approach in order to determine useful use cases and evaluate their contribution toward data security, transaction speed, and making sure of confidence. Case studies that are included in this study include but are not limited to JP Morgan's Quorum blockchain technology. According to many studies, blockchain technology cuts transaction costs by enhancing security and transparency, hence encouraging innovation in new financial services and products. This page contributes to the growing debate on decentralized technology through the provision of relevant insight into the legislators, regulators, and financial institutions leading this technological revolution.
Abdullah Ayub Khan, Abdulmajeed Alsufyani, Nawal Alsufyani, Mohamad Afendee Mohamed ¡ 5 authors
Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.
In this paper, we presented BlockShare, a blockchain-based system developed to facilitate privacy-preserving data sharing across decentralized networks. The proposed system enables users to retain control over their sensitive data while enabling secure, verifiable sharing with authorized parties.We implemented an authenticated data structure (ADS) to support decentralized verification and utilized zero-knowledge proof mechanisms to validate conditions without exposing the underlying data. Experimental analysis demonstrated that BlockShare performs efficiently in constructing data structures, generating proofs, and verifying them with minimal computational overhead. The platform successfully reduced privacy risks and enhanced trust in cross-organization data exchanges.
In our Society public opinion surveys are necessary for understanding societal viewpoints, but the conventional polling platforms are at a high risk of forgery, data breaches and manipulation. To ensure the reliability and integrity of polling outcomes, there is a growing need for secure and transparent mechanisms [1]. This paper presents ElectraGuard, a blockchain-based online polling platform designed to deliver trustworthy, tamper-resistant, and user-friendly opinion polling. Built on the Ethereum blockchain using smart contracts, ElectraGuard ensures decentralised execution, voter anonymity, and one-response-per-participant integrity [2]. The system leverages cryptographic hashing and distributed ledger technology to record each submission immutably, preventing result falsification or post-hoc modification. Supporting multiple categories of institutional and organizational polls, the platform features a web-based interface that enables secure participation and real-time result visualization. Testing shows that ElectraGuard greatly improves the security, ability to check results, and trustworthiness of online polling, providing a strong base for clear and checkable digital surveys.
Introduction Maintaining traceability within the food supply chain is key to ensuring food safety, quality, and regulatory compliance. In recent years, digital technologiesâespecially blockchain â have been adopted to enhance transparency and trust in âfarm-to-forkâ traceability systems, reducing fraud risk and enhancing recall management and strengthening consumer trust. However, their adoption differs based on variability in technological readiness, economic viability, and regulatory requirements. Methods This paper provides a scoping review of the application of such digital tools to enhance traceability throughout the European agri-food supply chain. being applied across the European agri-food supply chain to improve traceability. Following PRISMA-ScR guidelines, we searched multiple databases (Web of Science, ProQuest, IEEE Xplore, Alcorze) for relevant literature and included 60 peer-reviewed studies (primarily 2010â2025) that met our criteria (focus on blockchain, IoT, AI, or big data in European food supply chain traceability). Results and Discussion Blockchain emerged as the most frequently studied technology for food traceability âappearing in over 40% of the selected studies âoften deployed in combination with IoT sensors, RFID tags, or QR codes to create end-to-end transparency. These digital interventions are reported to strengthen traceability and consumer trust, improve supply chain efficiency, and support sustainability initiatives. However, adoption remains uneven. Most studies describe conceptual frameworks or pilot implementations rather than fully realized systems, and real-world deployment is hampered by interoperability challenges, scalability issues, regulatory uncertainties, and high costs. In conclusion, blockchain-based traceability shows great promise for the European food sector, but targeted efforts are needed to overcome it. Systematic Review https://archive.org/details/osf-registrations-m34ve-v1 .
M Srinivas P, B Sudeepa K, Ananth Prabhu, Ravi Babu ¡ 5 authors
This work proposes ZKPass, an emerging authentication platform making use of Zero-Knowledge Proofs (ZKPs) and blockchain to provide passwordless, decentralized digital ID proofing. ZKPass does away with conventional passwords and crypto key storage by tying authentication to a human-intelligible user identifier (UID) and locally generating zk-SNARK proof on the user device. The platform comprises an off-chain deterministic mnemonic recovery phrase from the UID and device-specific secrets allowing for self-sovereign offline account recovery independent of centralized entities. ZKPass stores only hashed UIDs and device metadata on the blockchain, minimizing costs, maintaining privacy, and allowing multi-device management. On-device proof generation coupled with on-chain smart-contract-based verification provides rigorous security, preserves privacy, and makes the solution practically usable. Performance comparisons indicate better proof sizes, verification times, gas usage, and recovery success over prevailing ZKP-based ID solutions. ZKPass overcomes key drawbacks of prevailing decentralized ID solutions by fusing powerful crypto security with user-friendly design to bring reality closer to adoption potential in healthcare, financial services and government sectors.
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.
This Utilize an efficiency-oriented procedure this research compares the achievement of two important Ethereum token standards: ERC-20 and ERC-721. Examining these processes under various operating settings while applying improving performance plan of action is the main goal. The research uses a administer Ethereum test net environment to apply maximizing techniques like batch processing, gas-efficient code, and meta data squeezing in addition to implementing token standards. Measured and examined are important performance indicators such as gas consumption, execution time, transaction output, and storage efficiency. According to the results, ERC-20 tokens are better ordered in routine and high-volume transactions because of their straight forward logic, but ERC-721 tokens take more resources at first because of their single identifiers and metadata needful although they manifest noticeable gains after optimization. The research highlight the theme of choosing a settlement according to the demands of the application, supporting ERC-20 for exchangeable asset management and ERC-721 for use cases involving non-exchangeable assets. Besides, it provide developers and blockchain architects practical advice for creating segregated apps (dApps) that are scalable, economical, and result-oriented. Due to its straighter forward and harmonious token logic, ERC-20 performs better than ERC-721 in terms of gas ability, transaction throughput, and storage utilization, according to the contrast analysis. Due to specialized token IDs and metadata, ERC-721, which is deliberated for unique assets, has increased gas expenses and storage on high. However, ERC-721 speed is greatly boosted by improving methods including batch processing and metadata compression. If presentation improving are used, ERC-721 is more looked right on for handling non- fungible assets like NFTs, but ERC-20 is more appropriate for high-frequency, exchangeable transactions like payments and DeFi.
Decentralized file systems (DFS) offer secure and transparent and resilient data storage through the integration of peer-to-peer storage protocols and blockchain technologies. Such systems are, nevertheless, vulnerable to a range of threats to security at an application, smart-contract, or network level. We present a profound discussion of the weaknesses of a DFS developed based on IPFS as a file-storing engine, Ethereum as metadata storage, and MetaMask as authentication in this paper. With a combination of tools of superior quality in security-related analyses, like Slither, MythX, and Metasploit, we model attack-related situations in the form of reentrancy, content poisoning, and Sybil, as well as phishing. The evidence highlights the main threats, with chief ones being contract-logic defects, file-integrity breaks and session socialists. We suggest the relevant countermeasures, including the best practices in smart-contract writing, cryptographic assurance of content identifiers (CIDs), strong session management, and defences against nodes. The findings provide a security benchmark to system developers and researchers keen on enhancing the robustness of decentralized systems of storage.
Farhad Asgarov, Fatih Said Duran, Namig Samadov, Ĺerif BahtÄąyar
Cite this article as: F. Asgarov, F. S. Duran, N. Samadov and Ĺ. Bahtiyar, âBlockchain based ownership and DNS configuration with ethereum rollups,âElectrica, 25, 0051, doi: 10.5152/electrica.2025.25005.
In this work, we present homomorphic encryption-based vaults (Haults), a permissioned privacy-preserving smart wallet protocol for VM-enabled blockchains that keeps users' balances confidential, as well as the amounts transacted to other parties. To comply with regulations, we include optional compliance features that allow specific entities (the auditors) to retrieve transaction amounts or execute force transfers when necessary. Our solution uses ElGamal over elliptic curves to encrypt balances, combined with zero-knowledge proofs to verify the correctness of transaction amounts and the integrity of the sender's updated balance, among other security checks. We provide a detailed explanation of the protocol, including a security discussion and benchmarks from our proof-of-concept implementation, which yield great results. Beyond in-contract issued tokens, we also provide a thorough explanation on how our solution can be compatible with external ones (e.g., Ether or any ERC20).
The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoinâs return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024âincluding multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventionsâare analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoinâs sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.
Smart contracts suffer from critical vulnerabilities such as reentrancy attacks, delegate call misuse, and timestamp dependencies, posing significant risks when deployed in smart grids. This paper proposes a solution that integrates deep learning techniques based on the Graph Isomorphism Network (GIN) to analyze Solidity code and detect vulnerabilities before deployment. This model was trained on a dataset of vulnerable contracts, leveraging automated hyperparameter optimization via Optuna to fine-tune the modelâs performance. Performance evaluations supported by visual analysis of the modelâs pre- and post-training weights provide essential insights into the behavior of the proposed approach. The experimental results demonstrated the effectiveness of GIN in identifying vulnerabilities with an accuracy of 95.26%, outperforming previous studies by more than 2%. These new findings highlight the potential of deep learning to secure and develop resilient smart grid infrastructures.
Blockchain technology has emerged as a transformative paradigm for decentralized systems, enabling secure, transparent, and tamper-resistant data management through distributed consensus mechanisms that eliminate the need for centralized control. At the core of these systems, consensus protocols ensure agreement among network participants; however, traditional approaches such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) face persistent challenges related to scalability, energy consumption, and latency. In response, graph-theoretic approaches have gained prominence as an effective framework for modeling and optimizing blockchain consensus by representing nodes as vertices and communication links as edges, thereby capturing complex network relationships, trust structures, and interaction patterns. This paper systematically reviews graph-based methods applied to blockchain consensus, highlighting their role in improving efficiency, enhancing security against attacks such as Sybil and double-spending, and optimizing node selection. Advanced techniques including graph partitioning, spectral clustering, and network flow optimization further contribute to improved scalability and throughput. The study identifies a clear transition toward intelligent, hybrid consensus mechanisms integrating graph theory, machine learning, and distributed computing, while also addressing ongoing challenges such as computational complexity and dynamic adaptability, and outlining future directions for AI-driven, scalable, and secure consensus models.
Joel Curado Silveirinha, Manila Bhandari, JoĂŁo C. Ferreira, Ana Martins
Despite the maritime supply chain being the backbone of global trade, it faces persistent challenges in transparency, fraud prevention, shipment tracking and data privacy. Blockchain technology has emerged as a transformative solution, enhancing trust and traceability within supply chain networks. However, its limitations in data privacy and scalability necessitate advanced privacy-preserving mechanisms. Zero-Knowledge Proofs (ZKP) offers a cryptographic approach to validate data without exposing sensitive information, addressing blockchainâs privacy constraints. This paper reviews the state of the art on current applications of blockchain in maritime supply chain management and explores the integration of ZKP for secure trade document verification, fraud detection, privacy-preserving traceability and regulatory compliance. Additionally, it examines computational overhead, scalability and adoption barriers while proposing future research directions. Implementing ZKP within blockchain-based port operations enables robust governance models, ensuring data verification without revealing confidential details. This approach fosters a secure and privacy-compliant trade environment, enhancing trust and collaboration among stakeholders. By optimising resource allocation and mitigating risks, integrating ZKP can significantly improve maritime supply chain efficiency. Integrating Zero-Knowledge Proofs with blockchain, maritime logistics can achieve a balance between transparency, security and operational efficiency, addressing existing challenges in data privacy and regulatory compliance, improving the sustainability of port operations.
The rapid advancement of artificial intelligence (AI) has begun to challenge traditional assumptions of corporate organization, governance, and commerce. While AI is widely recognized as a tool for enhancing decision-making and operational efficiency, an emerging possibility lies in the concept of AI corporationsâautonomous economic entities capable of engaging in trade, investment, and contractual relationships without direct human intervention. This paper explores the rise of AI corporations and their potential to redefine global commerce through autonomous economic agents. The study adopts a descriptive and analytical framework, drawing on secondary data, global case studies of decentralized autonomous organizations (DAOs), AI-driven financial institutions, and blockchain-enabled smart contracts. Findings suggest that AI corporations could significantly reduce transaction costs, enable borderless 24/7 trade, and enhance economic efficiency while simultaneously raising profound challenges concerning legal identity, accountability, taxation, and regulatory oversight. Unlike traditional corporations that rely on human managers and shareholders, AI corporations operate on algorithmic autonomy, raising questions about liability, ethical conduct, and governance in the absence of human decision-makers. The implications are both economic and policy-oriented: while the integration of AI corporations could accelerate global trade and investment, unchecked autonomy could lead to monopolistic control, systemic risks, and destabilization of labor markets. The paper argues for the urgent development of international regulatory frameworks, AI-specific corporate laws, and hybrid humanâAI governance models to harness the opportunities while mitigating risks. By positioning AI corporations as the next stage in the evolution of commerceâfrom traditional enterprises to digital platforms and now autonomous entitiesâthis study contributes to the discourse on the future of global business, law, and economic systems.