Ahmed Abubakar Aliyu, Mohammed Ibrahim, Saâadatu Abdulkadir
Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and selfâadaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long ShortâTerm Memory (LSTM) core to discern patterns and enhance its realâtime threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, selfâupdating neural networks are embedded in each block of the blockchain, forming a decentralized âbrainâ that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamperâproof operation and effective threat detection. Realâworld evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the systemâs superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.
P Siddharth Krishna, Mohamed Mustaf Ahmed, Mohammed Abdul Wasey, Mrs. Hyma Birudaraju
AbstractâEnsuring compliance with regulatory requirements through efficient Know Your Customer (KYC) processes continues to pose a major challenge for financial institutions globally. Traditional KYC procedures are often inefficient, repetitive, and exposed to significant security risks such as identity fraud, data breaches, and inconsistent verification protocols. As consumer expectations for smooth digital banking interactions rise, current systems frequently fail to balance both security and operational efficiency. This paper introduces a blockchain-driven solution built on the Ethereum platform aimed at enhancing the security and effectiveness of KYC operations. By leveraging blockchainâs decentralized structure, customer data is stored on a distributed ledger, which guarantees data integrity, transparency, and resistance to tampering. The proposed model also grants individuals greater control over their personal information, allowing them to selectively share data with financial institutions, thereby minimizing redundancy and strengthening privacy. Smart contracts are integrated into the system to automate the KYC verification steps, ensuring swift processing aligned with regulatory standards. Additionally, a central banking body is entrusted with managing a complete directory of all involved financial entities and monitoring their compliance with the rules enforced within the blockchain ecosystem.
This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.
Understanding regime shifts in crypto asset markets is essential for anticipating systemic risk and enhancing real-time monitoring tools. This study investigates structural changes in five major cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Solana (SOL), Aave (AAVE), and Bitcoin Cash (BCH)âover the 2023â2025 period. Using the Generalized Sup Augmented Dickey-Fuller (GSADF) test applied to daily high-frequency mid-price data, we assess the presence and timing of structural breaks in each asset. The results reveal that BTC and BCH experienced regime shifts that aligned with macroeconomic developments such as monetary policy announcements. In contrast, DeFi-related tokens (ETH, SOL, and AAVE) exhibited more fragmented and short-lived shifts, often driven by project-specific technical changes. Notably, ETH showed a structural break in April 2024, likely related to Layer-2 migration pressures and delays in protocol upgrades. In April 2025, both the crypto asset market and traditional financial markets experienced substantial turbulence following heightened trade policy actions by the United States, which fueled global economic uncertainty. Despite these disturbances, the S&P 500 index did not exhibit persistent structural breaks, suggesting that traditional equity markets are more resilient to transient macroeconomic shocks. This contrast underscores Bitcoinâs emerging role as a macro-sensitive digital asset and highlights the structural volatility within decentralized finance ecosystems. Although the GSADF test is computationally intensive (O(T4)), we discuss future research directions involving GPU acceleration and surrogate modeling. Additionally, we propose the integration of LPPLS-based frameworks to support real-time detection of financial exuberance and contribute to more robust risk management strategies in volatile crypto-financial systems.
Decentralized blockchains have grown into massive and Internet-scale ecosystems, collectively securing hundreds of billions of dollars in value. The complex interplay of technology and economic incentives within blockchain systems creates a delicate balance that is susceptible to significant shifts even from minor changes. This paper underscores the importance of conducting thorough, data-driven studies to monitor and understand the impacts of significant shifts in blockchain systems, particularly focusing on Ethereumâs groundbreaking builderâproposer separation (PBS) as a pivotal innovation reshaping the ecosystem. PBS revolutionizes Ethereumâs block production, entrusting builders with block construction and proposers with validation via blockchain consensus, with significant impacts on Ethereum decentralization, fairness, and security. Our empirical study reveals key insights, including the following: (a) A substantial 261% increase in proposer revenue underscores the effectiveness of PBS in promoting widespread adoption, significantly enhancing block rewards and proposer incomes. (b) The small profits garnered by builders, comprising only a 3.5% share of block rewards, raise concerns that the security assumptions based on builder reputation may introduce new threats to the system. (c) PBS promotes a more equitable distribution of resources among network participants by reducing proposer centralization and preventing centralization trends among builders and relays, thereby significantly enhancing fairness and decentralization in the Ethereum ecosystem. This study provides a comprehensive analysis of the dynamics of Ethereum PBS adoption, exploring its effects on revenue redistribution among various participants and highlighting its implications for the Ethereum ecosystemâs decentralization.
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, the newly created transactions for deferred payments introduce additional time overheads, which weaken the scalability of BNPL services. To address these issues, we propose an efficient and privacy-preserving blockchain-based asynchronous payment scheme (Epass), which has promising scalability while protecting the privacy of on-chain consumer transactions. Specifically, Epass leverages locally verifiable signatures to guarantee the privacy of consumer transactions against malicious acts. Then, a privacy-preserving asynchronous payment scheme can be further constructed by leveraging time-release encryption to control trapdoors of redactable blockchain, reducing time overheads by modifying transactions for deferred payment. We give formal definitions and security models, generic structures, and formal proofs for Epass. Extensive comparisons and experimental analysis show that \textsf{Epass} achieves KB-level communication costs, and reduces time overhead by more than four times in comparisons with locally verifiable signatures and Go-Ethereum private test networks.
The purpose of the covert communication scheme is to conceal the communication behavior entirely. In such schemes, the sender and receiver rely on secret keys to establish a covert channel. However, conventional key exchange protocols would expose the key exchange process between them. An adversary who observes the key exchange would be aware of the existence of communication behavior. The keys used in covert communication are not suitable to be generated through conventional key exchange schemes. To address this, we propose a blockchain-based covert elliptic-curve Diffie-Hellman key exchange scheme (BCDH) to conceal the process of the key exchange in blockchain transactions. Following a straightforward setup, BCDH allows the sender and receiver to covertly exchange a secret key on a blockchain without direct communication. Furthermore, we expand the BCDH approach to operate across multiple blockchains, further enhancing its covertness and stability. We analyze BCDH from several perspectives, including covertness, security, randomness, etc. Additionally, we implement a prototype of BCDH on the Ethereum platform to assess its feasibility and performance. Our evaluation demonstrates that BCDH is efficient and well-suited for real-world applications.
Open access
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Bello Musa Yakubu, Abdullah Alabdulatif, Pattarasinee Bhattarakosol
The rice supply chain is a complex system that demands effective management to ensure reliability and efficiency, given the involvement of multiple stakeholders. Blockchain technology, with its decentralized and tamper-resistant nature, offers a promising solution for improving transparency, traceability, and credibility in agricultural supply chains. However, existing blockchain systems face several technological challenges, including security vulnerabilities, privacy concerns, and performance limitations. To address these issues, this article presents RiceChain-Plus, an enhanced architecture that incorporates a private Ethereum blockchain, proof of authority (PoA) consensus mechanism, mutual authentication, zero-knowledge proofs (ZKPs), a hybrid role-based access control (RBAC) and attribute-based access control (ABAC) system, and one-way hash functions. This approach enhances the rice supply chain's security, privacy, and efficiency by safeguarding sensitive data and ensuring confidentiality. Performance assessments show that RiceChain-Plus surpasses existing benchmark models, achieving the lowest average execution costs (44,634 gas), reduced energy consumption (9.38828E-05 J), higher throughput (0.071201 transactions/s), faster execution (44.5 ms), and quicker transaction times (14.045 s), while also improving scalability. A comprehensive security analysis further confirms the framework's resilience against various cyberattacks. These results highlight RiceChain-Plus as a secure, efficient, and effective solution for optimizing rice supply chain operations.
Dumitrel Loghin, Shuang Liang, S. Liu, Xiong Liu ¡ 6 authors
Zero-knowledge proofs (ZKP) are becoming a gold standard in scaling blockchains and bringing Web3 to life. At the same time, ZKP for transactions running on the Ethereum Virtual Machine require powerful servers with hundreds of CPU cores. The current zkProver implementation from Polygon is optimized for x86-64 CPUs by vectorizing key operations, such as Merkle tree building with Poseidon hashes over the Goldilocks field, with Advanced Vector Extensions (AVX and AVX512). With these optimizations, a ZKP for a batch of transactions is generated in less than two minutes. With the advent of cloud servers with ARM which are at least 10% cheaper than x86-64 servers and the implementation of ARM Scalable Vector Extension (SVE), we wonder if ARM servers can take over their x86-64 counterparts. Unfortunately, our analysis shows that current ARM CPUs are not a match for their x86-64 competitors. Graviton4 from Amazon Web Services (AWS) and Axion from Google Cloud Platform (GCP) are 1.6X and 1.4X slower compared to the latest AMD EPYC and Intel Xeon servers from AWS with AVX and AVX512, respectively, when building a Merkle tree with over four million leaves. This low performance is due to (1) smaller vector size in these ARM CPUs (128 bits versus 512 bits in AVX512) and (2) lower clock frequency. On the other hand, ARM SVE/SVE2 Instruction Set Architecture (ISA) is at least as powerful as AVX/AVX512 but more flexible. Moreover, we estimate that increasing the vector size to 512 bits will enable higher performance in ARM CPUs compared to their x86-64 counterparts while maintaining their price advantage.
The security testing of Ethereum smart contracts has become increasingly important with the rise of decentralized applications (DApps) and blockchain technology. This systematic literature review (SLR) aims to provide a comprehensive overview of the state-of-the-art techniques, methodologies, tools, and challenges in the security testing of Ethereum smart contracts. By synthesizing and analyzing existing research articles, conference papers, and other relevant sources, this SLR identifies common trends, gaps, and areas for future research in this domain. The review covers various aspects of security testing, including vulnerability detection, testing frameworks, automated analysis tools, and best practices. In addition, it explores the impact of security vulnerabilities on smart contract ecosystems and proposes recommendations to improve the effectiveness and efficiency of security testing processes. This SLR serves as a valuable resource for researchers, practitioners, and developers interested in improving the security and reliability of Ethereum smart contracts.
This study seeks to offer an in-depth examination of cryptocurrency investments through the lens of Islamic law, with particular emphasis on assessing the Shariah compatibility of widely used digital assets such as Bitcoin and Ethereum. The novelty of this research lies in its systematic exploration of key issues such as the speculative nature, intrinsic value, and potential for financial harm (gharar) associated with cryptocurrencies. This study adopts a qualitative approach, drawing upon primary sources of Islamic jurisprudence namely the Quran, Hadith, and classical scholarly interpretations while also incorporating contemporary fatwas, insights from prominent Islamic finance scholars, and expert interviews to inform the analysis. The results highlight divergent viewpoints on the permissibility of cryptocurrency investments, with some scholars asserting their compliance under specific conditions, while others deem them non-compliant due to risks of speculation and uncertainty. The study concludes by proposing a set of actionable guidelines for Muslim investors, underscoring the significance of grasping the intricacies of Shariah principles in cryptocurrency investments and highlighting the necessity for continuous scholarly engagement in this evolving domain.
Vincenzo P. Di Perna, Marco Bernardo, Francesco Fabris, Sebastião Amaro ¡ 6 authors
Since blockchains are increasingly adopted in real-world applications, it is of paramount importance to evaluate their performance across diverse scenarios.Although the network infrastructure plays a fundamental role, its impact on performance remains largely unexplored.Some studies evaluate blockchain in cloud environments, but this approach is costly and difficult to reproduce.We propose a cost-effective and reproducible environment that supports both cluster-based setups and emulation capabilities and allows the underlying network topology to be easily modified.We evaluate five industry-grade blockchains -Algorand, Diem, Ethereum, Quorum, and Solana -across five network topologies -fat-tree, full mesh, hypercube, scale-free, and torus -and different realistic workloads -smart contract requests and transfer transactions.Our benchmark framework, Lilith, shows that full mesh, hypercube, and torus topologies improve blockchain performance under heavy workloads.Algorand and Diem perform consistently across the considered topologies, while Ethereum remains robust but slower.
The explosive growth of Non-Fungible Tokens (NFTs) has revolutionized digital ownership by enabling the creation, exchange, and monetization of unique assets on blockchain networks. However, this surge in popularity has also given rise to a disturbing trend: the emergence of rug pulls - fraudulent schemes where developers exploit trust and smart contract privileges to drain user funds or invalidate asset ownership. Central to many of these scams are hidden backdoors embedded within NFT smart contracts. Unlike unintentional bugs, these backdoors are deliberately coded and often obfuscated to bypass traditional audits and exploit investor confidence. In this paper, we present a large-scale static analysis of 49,940 verified NFT smart contracts using Slither, a static analysis framework, to uncover latent vulnerabilities commonly linked to rug pulls. We introduce a custom risk scoring model that classifies contracts into high, medium, or low risk tiers based on the presence and severity of rug pull indicators. Our dataset was derived from verified contracts on the Ethereum mainnet, and we generate multiple visualizations to highlight red flag clusters, issue prevalence, and co-occurrence of critical vulnerabilities. While we do not perform live exploits, our results reveal how malicious patterns often missed by simple reviews can be surfaced through static analysis at scale. We conclude by offering mitigation strategies for developers, marketplaces, and auditors to enhance smart contract security. By exposing how hidden backdoors manifest in real-world smart contracts, this work contributes a practical foundation for detecting and mitigating NFT rug pulls through scalable automated analysis.
Smart contracts are self-executing programs that facilitate trustless transactions between multiple parties, most commonly deployed on the Ethereum blockchain. They have become integral to decentralized applications in areas such as voting, digital agreements, and financial systems. However, the immutable and transparent nature of smart contracts makes security vulnerabilities especially critical, as deployed contracts cannot be modified. Security flaws have led to substantial financial losses, underscoring the need for robust verification before deployment. This survey presents a comprehensive review of the state of the art in smart contract security verification, with a focus on Ethereum. We analyze a wide range of verification methods, including static and dynamic analysis, formal verification, and machine learning, and evaluate 62 open-source tools across their detection accuracy, efficiency, and usability. In addition, we highlight emerging trends, challenges, and the need for cross-methodological integration and benchmarking. Our findings aim to guide researchers, developers, and security auditors in selecting and advancing effective verification approaches for building secure and reliable smart contracts.
The integration of Artificial Intelligence (AI) into decentralized finance (DeFi) has triggered a paradigm shift in the automation and optimization of financial contracts, particularly within the domain of financial derivatives. Derivatives, including options, futures, swaps, and forwards, are among the most complex financial instruments, requiring accurate pricing, efficient settlement, and continuous risk monitoring. Smart contractsâself-executing agreements coded onto blockchain networksâhave emerged as a transformative mechanism to automate these processes. However, conventional smart contracts in DeFi are constrained by inefficiencies in execution logic, gas costs, vulnerability to adversarial trading strategies, and limitations in adapting to real-time market fluctuations. This manuscript investigates AI-driven optimization frameworks for smart contracts in derivatives markets, where machine learning algorithms, reinforcement learning agents, and predictive analytics are employed to dynamically enhance pricing mechanisms, counterparty risk management, and execution efficiency. The study builds on an extensive literature review of DeFi, AI-finance integration, and blockchain automation, proposing an AI-augmented smart contract architecture that enables adaptive fee structures, risk-adjusted margin calls, automated dispute resolution, and latency-sensitive derivatives clearing. A simulation-based methodology was employed, where deep reinforcement learning models interacted with synthetic market data to optimize contract logic in futures and options markets deployed on Ethereum Virtual Machine (EVM)-compatible blockchains. Statistical evaluation revealed that AI-enhanced smart contracts demonstrated 25â40% improvement in transaction throughput, 18â25% reduction in gas costs, 30â35% enhancement in derivative pricing accuracy, and 50% reduction in settlement disputes compared to baseline blockchain contracts. The results highlight that AI-driven optimization is not only feasible but essential for scaling derivatives trading in DeFi to institutional-grade levels. The paper concludes by discussing regulatory implications, computational limitations, adversarial AI threats, and the future trajectory of autonomous financial engineering.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini ¡ 5 authors
In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.
Roman Kashitsyn, Robin KĂźnzler, Ognjen MariÄ, Lara Schmid
Reentrancy is a well-known source of smart contract bugs on Ethereum, leading e.g. to double-spending vulnerabilities in DeFi applications. But less is known about this problem in other blockchains, which can have significantly different execution models. Sharded blockchains in particular generally use an asynchronous messaging model that differs substantially from the synchronous and transactional model of Ethereum. We study the features of this model and its effect on reentrancy bugs on three examples: the Internet Computer (ICP) blockchain, NEAR Protocol, and MultiversX. We argue that this model, while useful for improving performance, also makes it easier to introduce reentrancy bugs. For example, reviews of the pre-production versions of some of the most critical ICP smart contracts found that 66% (10/15) of the reviewed contracts -- written by expert authors -- contained reentrancy bugs of medium or high severity, with potential damages in tens of millions of dollars. We evaluate existing Ethereum programming techniques (in particular the effects-checks-interactions pattern, and locking) to prevent reentrancy bugs in the context of this new messaging model and identify some issues with them. We then present novel Rust and Motoko patterns that can be leveraged on ICP to solve these issues. Finally, we demonstrate that the formal verification tool TLA+ can be used to find and eliminate such bugs in real world smart contracts on sharded blockchains.
Decentralized Finance (DeFi) has revolutionized traditional banking paradigms, offering transparent, peer-to-peer financial services without intermediaries. This paper presents a novel DeFi banking system that leverages advanced blockchain technologies including Solana and Ethereum networks, integrated through React, Node.js, and Metamask. The system facilitates seamless ETH transactions both sending and receiving across multiple networks using Hard Hat for simulation and testing. By implementing decentralized transaction history tracking, it aims to enhance transparency and user autonomy in digital banking. Our project addresses key issues of scalability, security, and ease of access, which are fundamental in current decentralized applications. We analyze the interplay between decentralized systems and traditional banking infrastructures, shedding light on how DeFi could offer faster, cheaper, and more secure financial services. Additionally, we discuss potential challenges, such as regulatory uncertainties and smart contract vulnerabilities, which need to be addressed for DeFi systems to gain widespread adoption. Through this system, we envision a future where DeFi can complement, rather than disrupt, traditional banking by providing secure, scalable, and user-centric financial services.
The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.
Introduction. Blockchain technology has emerged as a transformative innovation in distributed computing, providing a secure, transparent, and decentralized mechanism for data management. Initially introduced as the backbone of cryptocurrencies, blockchain has expanded into various sectors, including finance, healthcare, supply chain management, and governance. However, despite its numerous advantages, blockchain faces significant challenges, including scalability, transaction speed, and energy consumption. This article presents a comprehensive analysis of blockchain technology, focusing on its classification, consensus mechanisms, scalability solutions, and future trends. The study explores the comparative advantages and limitations of different blockchain architectures and evaluates emerging optimization techniques such as hybrid consensus algorithms and artificial intelligence-based enhancements. Purpose of the Work. The objective of this study is to conduct an in-depth analysis of blockchain technology, investigating its core principles, operational mechanisms, and performance optimization strategies. The research aims to provide a systematic comparison of consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Byzantine Fault Tolerance (BFT) variations, assessing their impact on transaction speed, energy efficiency, and security. Additionally, the study examines Layer 1 (L1) and Layer 2 (L2) scaling solutions such as sharding, rollups, and sidechains to address blockchain's scalability challenges. The research also highlights emerging trends in blockchain development, particularly hybrid models and AI-driven optimization techniques, which can enhance blockchain efficiency and security. Results. The analysis reveals that different blockchain architectures exhibit varying trade-offs between decentralization, security, and scalability. Public blockchains, such as Bitcoin and Ethereum, prioritize decentralization and security but suffer from limited scalability. Private blockchains, in contrast, offer higher transaction throughput but compromise decentralization. Hybrid blockchains aim to balance these aspects by integrating the strengths of both models. A detailed comparison of consensus mechanisms indicates that PoW, while highly secure, is energy-intensive and slow, whereas PoS and its variations provide faster and more energy-efficient alternatives. The study also finds that Byzantine Fault Tolerance-based mechanisms, such as PBFT and DBFT, offer high-speed consensus suitable for enterprise applications. Furthermore, Layer 1 improvements, including sharding, enhance on-chain transaction processing, while Layer 2 solutions, such as optimistic rollups and zero-knowledge rollups, significantly increase throughput by offloading computations to secondary layers. The research highlights recent advancements, such as AI-assisted transaction validation and adaptive consensus algorithms, as promising directions for blockchain scalability and security. Conclusions. The study underscores the importance of optimizing blockchain scalability and consensus mechanisms to enable broader adoption across industries. While Layer 1 and Layer 2 solutions provide significant improvements in throughput and efficiency, their integration remains a key challenge. The findings suggest that hybrid consensus models and AI-based optimizations could further enhance blockchain performance, reducing energy consumption while maintaining security and decentralization. Future research should focus on developing dynamic sharding techniques, parallel consensus mechanisms, and predictive analytics for transaction management to advance blockchain's applicability in large-scale real-world scenarios. The continued evolution of blockchain technology will play a critical role in shaping secure, efficient, and decentralized digital ecosystems. Keywords: blockchain, decentralization, consensus mechanisms, optimistic rollups, sharding, transaction validation.
Femi Oke, Samuel A Adeniji, Oyeneye Bolaji, Oladipupo Dopamu ¡ 5 authors
Oncology care and research demand robust patient consent management to balance data sharing with privacy. This article explores a theoretical framework for implementing blockchain-based patient consent management within FHIR-compliant oncology platforms. The proposed approach integrates distributed ledger technology (using frameworks such as Hyperledger Fabric and Ethereum) with HL7 FHIR standards (including SMART on FHIR for authorization) to create a tamper-proof, interoperable consent system. We describe how patient consent directives can be recorded as smart contracts or transactions on a blockchain while remaining aligned with FHIR's Consent resource, enabling seamless data exchange across clinical and research systems. We discuss technical integration points, such as using OAuth2 (SMART on FHIR) for patient-facing consent apps and leveraging blockchain to serve as a decentralized consent registry. Key issues are examined, notably interoperability (ensuring compatibility with existing health IT and standards), privacy (protecting patient data and complying with regulations), patient empowerment (giving patients greater control and transparency), and alignment with national/international healthcare data standards and policies. Real-world examples from current literature are cited to substantiate the feasibility and advantages of this approach. The analysis indicates that a blockchain-enabled consent management system could enhance trust, security, and patient-centric control in oncology data sharing, though challenges in scalability, governance, and regulatory acceptance remain.
Keamanan dan transparansi dalam sistem pemungutan suara merupakan tantangan utama dalam pemilihan elektronik atau e-voting. Penelitian ini bertujuan untuk mengimplementasikan smart contract blockchain Ethereum pada aplikasi evoting dengan tujuan meningkatkan keamanan, integritas, dan transparansi proses pemilihan. Metode penelitian yang digunakan mencakup perancangan dan implementasi smart contract, deployment pada jaringan Ethereum Sepolia, serta pengujian keamanan dan efisiensi biaya transaksi menggunakan alat seperti Remix IDE. Hasil penelitian menunjukkan bahwa penggunaan smart contract tercatat dengan transparan dan tidak bisa dimanipulasi. Namun, ada beberapa tantangan yang ditemukan seperti biaya transaksi atau gas fee yang cukup tinggi dan kompleksitas penggunaan aplikasi bagi pengguna yang belum familiar dengan teknologi blockchain. Pengujian keamanan mengidentifikasi beberapa isu yang perlu dioptimalkan, termasuk efisiensi penggunaan gas fee dan mengurangi potensi kerentanan dalam smart contract. Dengan demikian, penelitian ini dapat menjadi dasar untuk pengembangan lebih lanjut dalam penerapan sistem e-voting berbasis blockchain, khususnya untuk pemilihan skala besar seperti pemilu lokal atau nasional.
Blockchain's economic value lies in enabling financial and economic transactions without relying on trusted, centralized intermediaries. In practice, however, transactions pass through a fragmented chain of intermediaries before being included on-chain. Because standard blockchain data reveal only the winning block, this process is largely unobservable. We address this limitation by constructing a novel dataset of 15,097 non-winning Ethereum blocks, that is, blocks proposed but not selected for inclusion. We show that 21% of user transactions are delayed: they appear in candidate blocks but not in the winning block, implying that fragmented routing materially affects inclusion time. We further show that execution quality varies substantially across candidate blocks: for the same swap, both execution probability and execution price differ across proposed blocks. To study these differences, we examine competition between two arbitrage bots trading between decentralized and centralized exchanges. We find that, conditional on inclusion in a block that also contains transactions from these bots, user swaps in the same (opposite) direction are less likely (more likely) to execute and receive worse (better) prices. These results show that routing and block composition are central determinants of execution quality and market quality in on-chain markets.
In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners' data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners' activities, and predicting learners' academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner's data on the blockchain through the interplanetary file system and reap the benefits of securing the learner's data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner's performance. The smart contract functions also play a role in enabling the university to issue learners' certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner's performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain's functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.