Follow blockchain research across journals, conferences, and preprint repositories.
52,560 results · page 270 of 2,190
Cristina Carata, William J. Knottenbelt
No abstract is available for this record.
Daniel Ferreira
ABSTRACT Research Question/Issue Blockchain technology promises to revolutionize governance through strong commitments, trustlessness, and transparency. This paper examines how these promises have failed to materialize in practice. Research Findings/Insights Drawing on case evidence from major blockchains, including Bitcoin and Ethereum, I argue that blockchains have evolved into technocracies where developers, foundations, and companies exercise disproportionate control. Rather than being exceptional, blockchain governance suffers from the same coordination problems, collective action failures, and centralization tendencies that plague traditional governance systems. Theoretical/Academic Implications The paper concludes that while blockchains offer valuable experiments in governance design, their alleged advantages over traditional institutions remain largely mythical. Practitioner/Policy Implications Blockchain organizations should acknowledge their reliance on off‐chain coordination and informal authority. Investors must understand that blockchain governance depends on trusting technical elites, while regulators should recognize that decentralization claims often mask concentrated power structures requiring traditional oversight.
Akmam Majed Mosa
Decentralized finance (DeFi) protocols are becoming increasingly targeted by cyber threats, such as liquidity drain attacks, smart contracts flaws that leverage instant loans, and increasingly sophisticated threats that include DarkGate ransomware. We develop a hybrid framework that integrates CTI and predictive analytics to facilitate improving consensus mechanisms in a blockchain network. The proposed framework is centered on three layers , a data collection and processing layer, a security oracle layer that engages to mitigate intervention, and a dynamic adaptive mechanism to reach consensus. A 250-node testbed was built and deployed with the Hyperledger Besu and Geth deployments of Ethereum incorporating hybrid GRU-BiLSTM which utilize GNN's for predicting attacks. The results reveal improvements of transaction processing TPS of up to +236%, settlement latency improved -75%, fork rate improved to less than 3%, and downtime improved from 15% to 1.5%. Statistical tests T-Test and ANOVA also reveal these were of high statistically significance at p < 0.01. This study emphasizes that bridging functional aspects of AI with adaptive consensus mechanisms will be an effective approach at combating advanced cyber-attacks while maintaining reliability and resilience in DeFi systems.
Sani Lawal
The convergence of blockchain and Zero Trust Architecture (ZTA) offers a transformative pathway for enhancing security and resilience in financial infrastructures. Traditional network perimeter models are increasingly inadequate for safeguarding decentralized finance (DeFi), payment systems, and digital asset platforms that operate across distributed environments. This study explores how Zero Trust principles rooted in continuous verification, least privilege access, and micro-segmentation can be effectively integrated into blockchain ecosystems to mitigate identity spoofing, insider threats, and data tampering. By examining hybrid frameworks that combine permissioned blockchains with Zero Trust access controls, the research highlights a paradigm shift toward adaptive, identity-centric security postures in financial networks. The proposed model emphasizes dynamic authentication, real-time monitoring, and cryptographic assurance to ensure trustless yet verifiable interactions among nodes and participants. This integration not only fortifies compliance with emerging regulatory standards but also enhances interoperability and transparency across multi-chain financial systems. The findings suggest that embedding Zero Trust principles within blockchain-oriented infrastructures can create a self-healing, auditable, and future-ready digital finance ecosystem.
Idris Olanrewaju Ibraheem, Abdulrauf Tosho, Kamil Saka, Bolakale Lawal Aremu
Blockchain technology has revolutionized digital transactions by offering decentralization, transparency, and immutability. However, its inherent transparency often conflicts with the need for user privacy and anonymity, raising significant concerns regarding accountability, especially in regulatory and legal contexts. This study explores the delicate balance between anonymity and accountability in blockchain systems, proposing a framework that ensures both privacy and compliance with regulatory requirements. The research addresses key challenges in balancing these two aspects, evaluates the effectiveness of existing privacy-preserving technologies such as zero-knowledge proofs and ring signatures, and introduces the Privacy-Accountability Balanced Blockchain (PABB) Framework. This framework integrates Selective De-Anonymization, Self-Sovereign Identity (SSI), and the Adaptive Privacy-Accountability Control (APAC) Algorithm to dynamically adjust privacy levels based on regulatory conditions. Through theoretical analysis, mathematical modeling, and empirical validation, preserving privacy for 92% of transactions while enabling selective de-anonymization in high-risk cases, the study demonstrates that the APAC Algorithm effectively balances privacy and compliance needs. The findings suggest that privacy-conscious blockchain systems can coexist with accountability mechanisms, paving the way for ethical and legally sound blockchain applications. The study concludes that the PABB Framework offers a practical and scalable approach to achieving this balance, fostering trust among users and regulators alike.
Manaswini Bollikonda
The rapid expansion of multi-cloud ecosystems has intensified the demand for privacy-preserving analytics across untrusted infrastructures. This paper proposes Federated Zero-Trust Analytics (FZTA), a framework that integrates federated learning, zero-trust security, and privacy-enhancing computation to enable secure data collaboration without centralized trust. The design combines continuous identity verification, decentralized policy enforcement, and hybrid cryptography based on homomorphic encryption and differential privacy. Evaluation across three commercial clouds demonstrates that FZTA achieves near baseline model accuracy (within 2% of centralized training) while maintaining (ε&lt;1.2, δ=10−5) differential privacy guarantees and less than 20% computational overhead. The framework resists eavesdropping, replay, and model inversion attacks while meeting compliance standards such as GDPR and HIPAA. Results confirm that strong privacy and federated scalability can coexist under zero-trust conditions, establishing a foundation for secure cross-domain analytics in healthcare, finance, and IoT applications.
Sonali B. Wankhede, Dhiren Patel
Internet of Things (IoT) is transforming traditional agriculture into a more efficient, sustainable, and data-driven industry. By connecting various devices and sensors across the farm, IoT enables real-time monitoring, control, and optimization of agricultural processes. However, there are many security issues to deal with. IoT devices in precision farming collect sensitive data such as soil moisture, nutrient levels, and livestock health information. Unauthorized access to farm data can result in data theft, manipulation, or misuse. This can compromise the integrity of farming operations and potentially harm the environment. In this paper, we explore the mathematical foundations and practical implementations of model aggregation in federated learning (FL), with a particular focus on integration with distributed ledger technologies (DLT). We present a comprehensive analysis of aggregation algorithms, their convergence properties, and security guarantees. Additionally, we survey existing tools and platforms that facilitate federated learning deployments and examine how blockchain technology can address key challenges in federated learning systems including trust, incentive mechanisms, and auditability. Our analysis demonstrates that the combination of federated learning with blockchain creates a robust, transparent, and decentralized machine learning systems suitable for privacy-sensitive applications across precision farming, healthcare etc.
V. I. Lehenko
The article is devoted to the study of the current legal regulation of virtual assets in the Federal Republic of Germany. The author analyses the advantages and disadvantages of the relevant regulatory framework, decisions taken to harmonise legislation in accordance with the new Regulation of the European Parliament and of the Council, as well as the possibility and expediency of implementing the most successful decisions into Ukrainian legislation. Due to the lack of relevant in-depth studies that would combine the main regulatory norms and definitions, as well as provide a general overview of this regulatory system, it became necessary to conduct a detailed study of the current regulatory framework of the Federal Republic of Germany in this area, which is one of the most complex among known jurisdictions in the field of virtual assets. The following list details the responsible regulators, as well as the legally established definition of virtual assets and their classification. The Federal Republic of Germany has developed an original classification system and a hybrid approach to defining asset categories in order to apply the provisions of MiCA. Currently, not all objects created on the basis of blockchain technology are subject to regulation, primarily non-fungible tokens, which is in line with MiCA provisions. The licensing system for service providers in the field of virtual asset circulation, the specifics of the transition period and the new classification of licence classes in accordance with MiCA were also examined. An analysis of the requirements for initial coin offerings (ICOs) in accordance with the regulatory framework of the Federal Republic of Germany and MiCA was conducted. The issues of virtual asset mining regulation and taxation were examined. It is concluded that the Federal Republic of Germany has found a way to regulate many more assets than provided for by MiCA, which gives competitive advantages to the national economy. Ukrainian legislation needs to borrow the approach to building such a regulatory system, which can be harmonised with European Union legislation, while preserving the advantages of its own legislation.
Behnam Khayer, Siamak Mirzaei, Hooman Alavizadeh, Ahmad Salehi Shahraki
Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem.
Bowen He, Yufeng Hu, Zhuo Chen, Yuan Chen · 8 authors
The prosperity of Ethereum gives rise to a new type of transaction-based phishing scam. Specifically, users are tempted to visit phishing websites and sign phishing transactions that allow scammers to withdraw their tokens. Meanwhile, to accelerate the deployment of phishing websites, scammers have introduced a business model, Drainer-as-a-Service (DaaS). In this model, drainer operators focus on crafting specialized phishing toolkits, named ''wallet drainers'', while drainer affiliates handle the deployment and promotion of phishing websites. After stealing victims' tokens, they will distribute profits. In this paper, we present the first systematic study of DaaS on Ethereum. To begin with, we propose a snowball sampling approach to build the first large-scale DaaS dataset, including 1,910 profit sharing contracts, 56 operator accounts, 6,087 affiliate accounts, and 87,077 profit-sharing transactions. Then, we analyze the scale of DaaS from the perspectives of victims, operators, and affiliates, and perform clustering analysis to uncover dominant DaaS families. Finally, we reported DaaS accounts in the dataset and 32,819 phishing websites deployed with DaaS toolkits to the community. Our work aims to serve as a guide for Ethereum service providers to enhance user protection against DaaS.
Saenko, Elena
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Digital Transformation
Adrian Leonard Mociulschi
This article examines blockchain governance as a cultural and algorithmic paradigm, moving beyond code to constitutional logic. It analyzes Delegated Proof of Stake (DPoS) as a form of programmed democracy, highlighting its tension between efficiency and fairness and its risk of oligarchic drift. The study contrasts algorithmic transparency with centralized control in Web2, framing blockchain as a new public square where trust flows through cryptography rather than ballots. Cryptocurrencies are interpreted as algorithmic artifacts—symbols of a democratized code economy—while cases like Dogecoin reveal how humor and creativity shape value systems in decentralized networks. Ultimately, the paper argues that blockchain governance requires not only technical precision but symbolic legitimacy, balancing innovation with ethical responsibility.
Jamila Tileubaevna Arzieva, Ali Tileubaevich Arziev, Nawrızbay Baxtiyar ul Seytniyazov, Ilham Kongratbay ulı Tlemisov
THE ROLE OF ZERO-KNOWLEDGE PROOFS IN ENHANCING CRYPTOGRAPHIC PROTOCOLS // Universum: технические науки : электрон. научн. журн. Arzieva J.T. [и др.]. 2025. 10(139). URL: https://7universum.com/ru/tech/archive/item/21052
Dr. Muhammad Qasim Memon,Dr. Muhammad Naeem Akhter,Dr. Santosh Kumar Banbhrani,Zeeshan Qureshi
No abstract is available for this record.
Denis Wapukha Walumbe, Gabriel Ndugu Kamau, Jane Wanjiru Njuki
Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.
Evgeny Ukhanov
The proliferation of decentralized financial (DeFi) systems and smart contracts has underscored the critical need for software correctness. Bugs in such systems can lead to catastrophic financial losses. Formal verification offers a path to achieving mathematical certainty about software behavior. This paper presents the formal verification of the core logic for a token sale launchpad, implemented and proven correct using the Dafny programming language and verification system. We detail a compositional, bottom-up verification strategy, beginning with the proof of fundamental non-linear integer arithmetic properties, and building upon them to verify complex business logic, including asset conversion, time-based discounts, and capped-sale refund mechanics. The principal contributions are the formal proofs of critical safety and lifecycle properties. Most notably, we prove that refunds in a capped sale can never exceed the user's original deposit amount, and that the precision loss in round-trip financial calculations is strictly bounded. Furthermore, we verify the complete lifecycle logic, including user withdrawals under various sale mechanics and the correctness of post-sale token allocation, vesting, and claiming. This work serves as a comprehensive case study in applying rigorous verification techniques to build high-assurance financial software.
Quan Gu, Han Ye, Junjie Chen, Xiongfeng Ma
Quantum computers have the potential to break classical cryptographic systems by efficiently solving problems such as the elliptic curve discrete logarithm problem using Shor's algorithm. While resource estimates for factoring-based cryptanalysis are well established, comparable evaluations for Shor's elliptic curve algorithm under realistic architectural constraints remain limited. In this work, we propose a carry-lookahead quantum adder that achieves Toffoli depth $\log n + \log\log n + O(1)$ with only $O(n)$ ancillas, matching state-of-the-art performance in depth while avoiding the prohibitive $O(n\log n)$ space overhead of existing approaches. Importantly, our design is naturally compatible with the two-dimensional nearest-neighbor architectures and introduce only a constant-factor overhead. Further, we perform a comprehensive resource analysis of Shor's elliptic curve algorithm on two-dimensional lattices using the improved adder. By leveraging dynamic circuit techniques with mid-circuit measurements and classically controlled operations, our construction incorporates the windowed method, Montgomery representation, and quantum tables, and substantially reduces the overhead of long-range gates. For cryptographically relevant parameters, we provide precise resource estimates. In particular, breaking the NIST P-256 curve, which underlies most modern public-key infrastructures and the security of Bitcoin, requires about $4300$ logical qubits and logical Toffoli fidelity about $10^{-9}$. These results establish new benchmarks for efficient quantum arithmetic and provide concrete guidance toward the experimental realization of Shor's elliptic curve algorithm.
Yisong Chen, Chuqing Zhao, Yifan Gao
Introduction This systematic review comprehensively examines the application of blockchain in health insurance, highlighting the current state of research, inherent challenges, and future trends. Blockchains have demonstrated excellent potential in health insurance, especially in fraud prevention, claims processing, and data security. Moreover, this study also aims to identify several critical challenges that hinder their broader adoption and effectiveness, including data heterogeneity, individual task requirements, scalability and regulatory alignment. Methods System Review Results and Discussion Through comprehensive analysis, this study discusses some actionable solutions and strategies. Conclusively, this review underscores blockchain’s transformative impact on building a secure, efficient, and patient-centric health insurance system.
Lorena Baigorria, Ana Gabriela Garis, Daniel Eduardo Riesco
Blockchain es la base tecnológica de una nueva forma de realiz-ar transacciones de manera segura en una red descentralizada. Dicha tecnología permite registrar la validez y el origen de los datos, y realizar transacciones de manera digital, compartida, inalterable y sin la intervención de intermediarios. Frecuentemente, las transacciones requieren de lógica automa-tizada. En estos casos, se vuelve necesaria la definición de con-tratos inteligentes, programas de computación almacenados en Blockchain que se ejecutan automáticamente cuando se cumplen condiciones predeterminadas. Los errores en contra-tos inteligentes pueden tener graves consecuencias, especial-mente en ámbitos como finanzas descentralizadas. Una clara definición de las condiciones es esencial; sin embargo, éstas son generalmente descriptas en lenguaje natural por las partes involucradas, lo que conlleva a la ambigüedad de interpretación por parte de los programadores del contrato. Por otro lado, los errores en la programación también pueden derivar a que el contrato no se ejecute como se esperaba. La calidad del contra-to inteligente podría ser mejorada si las condiciones fueran especificadas en UML con OCL, y luego transformadas al len-guaje de modelado Alloy para llevar a cabo la verificación y validación formal a través del método Model Checking. En este artículo, se describe una línea de investigación que propone un modelo para la especificación de contratos inteligentes en UML y OCL, complementado con una transformación automática a Alloy para su verificación y validación. Dicho modelo contribuye a realizar una auditoría más rigurosa de contratos inteligentes antes de despliegue en Blockchain.
Javier Godoy, E. López Torres, Juan Pablo Galeotti, Diego Garbervetsky · 5 authors
No abstract is available for this record.
Ahmad A Alsharidah, Devki Nandan Jha, Ellis Solaiman, Bo Wei · 6 authors
Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.
Soubhagya Ranjan Mallick, Veena Goswami, Rakesh Kumar Lenka, Rabindra K. Barik · 6 authors
A digital revolution is taking place in healthcare due to rising patient data volumes, concerns about privacy and security, and the need for interoperable solutions. The demand for trustworthy, scalable, and interoperable technologies has become essential in this fast-growing healthcare industry. Conventional centralised systems regularly encounter challenges meeting these requirements because of problems including storage structure, data privacy, security vulnerabilities, network complexity, and lack of scalability. Additionally, current blockchain implementations frequently face challenges with interoperability, trust management among several healthcare stakeholders, and throughput bottlenecks. To overcome these challenges, this paper introduces a novel framework called LIVER: a Lightweight Infrastructure for Verifiable Electronic Records that integrates Blockchain, InterPlanetary File System (IPFS), Edge computing, and Internet of Medical Things (IoMT) to address these issues. Distributed processing based on edge computing and IPFS, cryptographic methods, blockchain consensus mechanisms, and an immutable ledger allows it to be scalable, secure, interoperable, and boast tamper-proof data integrity. Furthermore, a unique queueing approach has been implemented to improve operational efficiency, which controls patient flow, decreases wait times, maximises resource utilisation, minimises healthcare delays, and enhances the patient experience. Finally, based on the experimental evaluation results, the LIVER framework reduces ledger size, facilitates enhanced data sharing, and improves the healthcare system’s efficiency, scalability, security, and performance. The goal is to provide a framework that can handle the computational and operational limitations of existing healthcare systems while still protecting patient privacy and allowing for scalability.
Allan Edgard Silva Freitas
Quantum computing threatens foundational cryptographic assumptions in today’s distributed ledgers, while application demands outgrow the throughput and latency ceilings of single-chain blockchains. Directed acyclic graph (DAG) ledgers unlock parallelism but raise new questions about ordering, security, and light-client viability. This position paper argues for a postquantum (PQ) DAG ledger that matches DAG concurrency with PQ-secure consensus and transactions, plus a privacy-preserving identity/reputation layer. We sketch the architecture, situate it against the literature, and enumerate some open challenges to be addressed for deployment at scale. A carefully engineered PQ DAG can provide credible security and performance in a quantum-enabled adversarial landscape.
Jeyakumar Samantha Tharani, E.Y.A. Charles, Punit Rathore, Zhé Hóu · 6 authors
Blockchain is a distributed ledger technology that provides pseudo-anonymity among participants to maintain privacy. However, malicious actors utilise this property to hide their illegal rewards received through cyber attacks, dark market trades, money laundering and Ponzi schemes. The recent confiscation by the FBI of more than $4 million USD worth of bitcoin from the ‘Silk Road’ dark marketplace indicates the scale of the problem faced by financial regulators and law enforcement authorities. Analysing and identifying harmful actors is, therefore, necessary to regulate the transactions of digital assets. Machine learning models can assist in detecting patterns and correlations between the actors in blockchain networks that may not be apparent through traditional methods. In blockchain networks, the number of actors linked to illegal activities is significantly smaller than that of regular activities. Also, only very limited labelled transaction data is available about these malicious actors. These limitations make it harder to train supervised learning models to provide real-time proactive responses. This article represents a pioneering effort in thoroughly examining the different unsupervised learning methods for clustering suspicious behaviour of actors within blockchain networks. The proposed unsupervised learning-based analysis considers metadata and interconnectivity information of blockchain transactions. The metadata contains time-based and amount-based information. Interconnectivity data represents centrality measures and embedding vectors of the blockchain network. The quality of the identified clusters is validated using internal and external cluster validation measures. The validation results were used to identify influential features using the eXplainable AI technique Shapley (ShAP) values. The results reveal that the features related to the spending and receiving transactions strongly influenced cluster identification. Overall, the centroid-based and connectivity-based approaches identified well-separated clusters for metadata and centrality-based features of blockchain transactions.