Blockchain Papers

I-Chan Chiu, Mao-Wei Hung, Kuang‐Chieh Yen

No abstract is available for this record.

Sylwia Nowak

Rules of origin are a core element of any free trade agreement, but their complexity can present significant challenges for efficient and compliant use. This paper discusses the challenges and opportunities in automating origin calculations for businesses involved in cross-border trade. It focuses on the role of Enterprise Resource Planning (ERP) systems, customs software and Long-Term Supplier Declarations (LTSDs) in simplifying compliance with preferential origin rules. Focusing on the United Kingdom’s trade, the paper outlines key factors businesses must consider to effectively automate origin management, such as rules interpretation, data quality, legal documentation and supplier cooperation. The potential roles of distributed ledger technology (DLT) and automation within customs declarations software are also explored.

Abhijeet Deshmukh, Vivek Mahale, Ashok T. Gaikwad

Abstract: Homomorphic encryption (HE) enables secure computations on encrypted data without decryption, offering a transformative solution for privacy-preserving computation. This review presents a ten-year retrospective (2014–2024) on HE’s evolution since Gentry’s 2009 fully homomorphic encryption (FHE) scheme, which introduced the concept of performing arbitrary computations on ciphertexts. Early schemes were hindered by inefficiencies like computational overhead and noise accumulation. Over the past decade, significant advancements have addressed these barriers. Schemes such as BGV, BFV, and CKKS have been developed for efficient integer and approximate real-number computations. Algorithmic innovations like optimized bootstrapping and improved noise management have reduced complexity. Hardware acceleration using GPUs and FPGAs has enhanced performance, while integration with secure multi-party computation and zero-knowledge proofs has broadened HE’s applicability. Applications now span privacy-preserving machine learning, genomic data analysis, and financial analytics. Toolkits such as SEAL, HElib, and PALISADE have improved accessibility for developers and researchers. Despite progress, challenges remain, including balancing efficiency and security, and improving usability for non-experts. The article also explores HE’s reliance on lattice-based problems like Learning With Errors (LWE) and Ring-LWE, which provide quantum resistance. As hybrid cryptographic models emerge, HE is increasingly recognized as a key component in securing sensitive data in the postquantum era. This review highlights HE’s maturation from a theoretical concept to a practical solution, demonstrating its potential as a cornerstone for secure, privacy-preserving computing across industries.

Rajneesh Kumar, Sharvan Kumar Garg

With the rapid digitalization of financial services-spanning mobile wallets, peer-to-peer lending, central bank digital currencies (CBDCs), and decentralized finance (DeFi)-security architectures must evolve to counter diverse and emerging threats. Building on our earlier FinTechSec++ design for FinTechs and CBDCs, this paper reconceives the framework as a [Simplified] foundational platform for wider financial ecosystems. The framework introduces: (i) modular cryptographic plugins tailored to domain-specific data, (ii) a policy orchestration engine for reconciling multi-jurisdictional rules, (iii) audit logs optimized for external analytics tools, and (iv) automated retraining pipelines for anomaly detection. [Simplified long sentence] Benchmarks on three prototypes (Micro Payment App, CBDC Sandbox, and DeFi DEX) demonstrate threat detection rates of 94-97%, encryption throughput gains of 1.7x-1.9x, and policy enforcement latencies below 60 ms. These findings establish FinTechSec++ as a practical foundation for future financial data security solutions.

Srinivas. D, C M Preeti, P. Tirumala, Nitesh Ghodichor · 6 authors

It is anticipated that blockchain-based technologies will significantly alter a wide range of corporate processes and applications, which will have a significant impact on ecommerce. This study introduces a blockchain-based ecommerce transaction model, emphasising how it might improve efficiency, security, and transparency. By merging the state-of-the-art Blockchain Consent Algorithm (BCA) with Distributed Ledger Technology (DLT), particularly Decentralised Identifiable Distributed Ledger Technology (DIDLT), this study investigates how to improve the security and privacy of ecommerce transactions. This study examines the ways in which BCA, a state-of-the-art consensus algorithm created for blockchain environments, and DIDLT, which integrates decentralised identity management with blockchain technology, cooperate to safeguard e-commerce transactions against data manipulation, payment fraud, and identity theft. By offering a thorough examination of their application, this study elucidates the potential of BCA and DIDLT to revolutionise the e-commerce security and safety sector. The network's safety efficacy is greatly increased by using the integrated DIDLT-BCA model, which leads to 99.1% security, up to 150 milliseconds in performance times, and times of mining of up to 0.99 seconds.

Rabees Paroshan, Srivaitheeswari.M, Abhishek Kumar.S.A, S. Pavithra

Quantum computing has positioned itself as a serious threat to traditional cryptography, undermining the very foundation of present-day methods of transaction and the popularly used digital payment systems. Here lies an interest in proposing a platform that can remedy these quantum-age risks present in payment mechanisms. The intended aim thus becomes that of building a secure and scalable payment system that uses quantum computing algorithms to train AI models for fraud detection in real-time, QKD to manage key security, and PQC to securely encrypt transaction data. For privacy, ZKP will be used to verify the transaction without revealing any details from it. Also, the platform will integrate multichain blockchains with quantum sharding, allowing separate processing, and distribution of transaction storage. Experimental results have shown that the proposed platform can resist quantum attacks, attain more accuracy in ratio detection, and improve the transaction speed more than the average blockchain solutions. The novelty of this research stands on the enhanced multichain blockchain architecture and improved Zero-Knowledge Proof protocols, which improve scalability and privacy. By bringing multichain architecture, quantum computing, QKD, and PQC into one model, this platform sets the benchmark for a secure, scalable, and affordable digital payment system.

Ravi Khatri, Prateek Pandey, Rahul Pachauri

This research tackles the challenges of non-independent and identically distributed (non-IID) data, socio-political inequalities in decentralized energy networks, and the unpredictable nature of renewable energy sources. It integrates blockchain technology with federated learning (FL) and game theory. Cluster-based FL paired with Shapley value allocation helps mitigate data heterogeneity, while a combined Stackelberg-Shapley model implemented via smart contracts facilitates adaptive pricing strategies. To safeguard user privacy, the system incorporates zero-knowledge proofs, differential privacy$(\varepsilon=0.5)$, and CKKS-based homomorphic encryption, achieving a 98% resistance rate against cyberattacks. Field tests in the EU's NER400 sandbox and blockchain-enabled microgrids in Kenya confirm the framework's effectiveness—achieving a 4.2% mean absolute percentage error (MAPE) in forecasting (improving from a 12% benchmark), curbing renewable energy certificate (REC) fraud by 89%, and cutting rural energy expenses by 40%. Leveraging a hybrid consensus model (PBFT with Sharding), the platform supports over 10,000 per second with sub-second latency, bridging interoperability gaps between Ethereum-based REC systems and Hyperledger platforms.

Xiaohong Chen, Grigore Roşu

No abstract is available for this record.

Mutahar Mujahid Mohammed, Hemasree Koganti, Abdul Hadi, Sai Krishna Akula · 6 authors

Traditional and digital voting systems both have their flaws, such as being vulnerable to fraud, having limited auditability, and being controlled by a central authority, which poses a growing threat to the honesty, openness, and safety of elections. This study seeks to solve the problem by exploring the potential of a voting system built on the blockchain that would guarantee voter anonymity, eliminate single points of failure, and offer end-to-end verifiability. A hybrid blockchain architecture is proposed, combining permissioned networks for high performance with public blockchain anchoring for transparency and fairness. A prototype implemented on Hyperledger Fabric was evaluated through simulated municipal elections with 10,000 virtual voters, achieving an average vote processing latency of 0.75 seconds, throughput of 4,000 votes per minute, 100% vote integrity, 99.97% system uptime, and full voter anonymity via zero-knowledge proofs. The results confirm that the proposed system can meet the performance, scalability, and privacy requirements for secure digital elections, while identifying key challenges—such as scalability, regulatory compliance, and digital inclusion—that must be addressed for real-world deployment.

Rui Han, Bin Yuan, Weizhong Qiang, Deqing Zou · 5 authors

The widespread use of IoT devices in the accommodation and hospitality sectors has created demand for temporary device-permission sharing and transfer. Prior work has largely focused on security issues in device permission sharing, with far less attention devoted to device permission transfer. However, inappropriate access control management during device permission transfer can also lead to violations of the users' expectations of control over their devices. For example, a malicious host retaining or regaining access to a camera after its permission has been transferred to a tenant. In this paper, we present the first systematic study on understanding and enhancing the security of device permission transfer in IoT leasing. To this end, we propose Forseti, a new authorization framework that leverages zero-knowledge proof and a decentralized ledger to ensure that the rights of both hosts and tenants are not violated. Our evaluation demonstrates that Forseti is effective, efficient, scalable, and compatible with existing IoT platforms.

Karim Ben Yahia

Purpose This paper aims to explore the challenges and opportunities of cryptocurrency adoption in Tunisia, focusing on the perspectives of users and professionals. Specifically, it seeks to investigate the underlying factors influencing the adoption and usage of cryptocurrencies in the Tunisian context, including regulatory, technological and socio-economic considerations. By conducting a comprehensive analysis of the motivations, perceptions and experiences of cryptocurrency users and professionals, this research aims to provide valuable insights into the dynamics of cryptocurrency adoption in emerging markets. Through a nuanced examination of these factors, the study ultimately seeks to inform policy decisions, industry practices and future research directions aimed at fostering the responsible and sustainable integration of cryptocurrencies and blockchain technologies into the Tunisian economy. Design/methodology/approach A qualitative approach was used, combining 18 in-depth interviews with professionals alongside netnographic research conducted within two Facebook groups and a Discord group. Data analysis was carried out using T-LAB Plus 2022 software to identify key barriers and motivations to cryptocurrency adoption. Findings The findings reveal four distinct categories of cryptocurrency enthusiasts, along with the primary obstacles to adoption, including regulatory uncertainty, risks of fraud and theft and legal ambiguity. Motivations for adoption include revolutionary sentiments, profit-driven motives and peer influence. Furthermore, blockchain technology is recognized for its potential to enhance transparency and drive economic growth in Tunisia, particularly in sectors such as finance, agriculture and public services. The study reveals key differences between users and professionals in cryptocurrency and blockchain adoption. Users are driven by revolutionary goals and financial gain, while professionals emphasize risks, regulatory ambiguity and the need for clear legal frameworks. This contrast underscores the need for balanced policies that consider both perspectives. Research limitations/implications Limitations of this research include the small sample size due to data confidentiality and the difficulty in recruiting cryptocurrency holders, many of whom were hesitant to participate due to legal concerns. A future quantitative study could further explore these findings and broaden the generalizability of the conclusions, particularly concerning blockchain technology’s potential to drive economic growth. Practical implications The findings of this study highlight the need for regulatory clarity and consumer protection measures to foster trust and legitimacy in the cryptocurrency and blockchain markets in Tunisia. Additionally, educational initiatives and support for blockchain-based projects could promote innovation and economic growth in the region. Social implications Addressing the barriers to cryptocurrency adoption could have significant social implications, including increased financial inclusion, economic empowerment and technological advancement in Tunisia. By fostering an environment conducive to cryptocurrency and blockchain use, the country could position itself as a hub for digital innovation in the region. Originality/value This study offers a unique insight into cryptocurrency adoption in Tunisia, exploring user perspectives in an emerging market facing structural challenges. By comparing user experiences with professional insights, it also sheds light on the divergent views within the ecosystem, offering a comprehensive understanding of the barriers and opportunities in cryptocurrency adoption.

Lê Thanh Hà

No abstract is available for this record.

Hangfeng He, Weijie J. Su

No abstract is available for this record.

Bora Buğra Sezer, Sedat Akleylek

No abstract is available for this record.

Palaniappan Sellappan, Kavitha Shanmugam, Vishnu Periyannan Palaniappan

The rise of non-fungible tokens (NFTs) has moved beyond digital art into decentralized finance (DeFi) for loans. NFTs are now commonly used as collateral in many decentralized lending platforms in DeFi. The paper evaluates three lending protocols: NFTfi, Arcade, and BendDAO in detail. These platforms show different methods for risk mitigation, liquidation, and borrower-lender interactions. This paper suggests a detailed risk assessment framework for NFT collateral in DeFi lending platforms. It highlights NFT illiquidity, high volatility, valuation uncertainty, and protocol design problems. The use of Layer -2 blockchain based lending model results in reduced gas fees and supports scalability. It is specifically optimized for networks like Polygon that offer higher throughput and lower operational costs. Layer-2 deployment facilitates faster processing speeds and significantly reduces transaction fees for users. The framework integrates interoperable blockchain protocols that support seamless NFT collateral migration across chains. This cross-chain operability increases platform liquidity and lending flexibility for decentralized finance participants. Dynamic, risk-aware smart contracts modify lending terms in real time using asset volatility indicators. They assess borrower risk profiles with on-chain data to maintain adaptive, secure lending conditions. Monte Carlo simulation is used to estimate default risks and delays in NFT liquidation. This simulation helps understand results when the market conditions keep changing. Findings say lending platforms need dynamic risk settings and strong pricing oracles for safety. Smart, borrower behavior-based lending strategies are also needed for steady growth in NFT backed DeFi lending. This paper also provides insights into use of advance techniques beyond smart contracts to enhance the system. This research gives useful ideas to DeFi developers, investors, and regulators handling NFT collateral. It helps people understand NFT lending risks better and enable us to design stronger lending systems.

Omar Dib, Shiyun Li, Zhengkun Li, Rouwaida Abdallah · 5 authors

Federated Learning (FL) offers a promising paradigm for privacy-preserving collaborative training, yet it remains highly vulnerable to adversarial behaviors, client unreliability, and challenges associated with non-independent and identically distributed (non-IID) data. Existing secure aggregation techniques, while preserving confidentiality, fail to guarantee the integrity and trustworthiness of model updates, leaving FL deployments exposed to poisoning and consistency attacks. This work introduces FL-SMPC++, a robust and privacy-preserving FL framework designed to address these challenges. The primary objective is to develop a scalable solution that ensures verifiable, privacy-preserving aggregation while mitigating malicious client behaviors, dropouts, and data heterogeneity. Our approach integrates Secure Multi-Party Computation (SMPC), Pedersen commitments, and zero-knowledge proofs (ZKPs) to cryptographically bind clients' submitted updates to their validation outcomes without revealing private data. We propose a dynamic client selection strategy based on shared validation performance, a dropout-tolerant threshold aggregation protocol, and a warm-up initialization phase to counteract non-IID distributions. Comprehensive experiments on MNIST, CIFAR-10, FEMNIST, and UCI Heart Disease show that FL-SMPC++ consistently outperforms FedAvg, FedProx, and FedNova. For example, under a label-flipping attack with 30% malicious clients on CIFAR-10 (non-IID), FL-SMPC++ achieves 78.9% accuracy compared to 67.4% for FedAvg, representing an absolute gain of 11.5%. Across datasets, the framework limits accuracy degradation to 6–8% under attack, while baselines suffer 13–20% losses. These results demonstrate that FL-SMPC++ achieves strong cryptographic privacy guarantees together with empirically validated resilience and convergence, offering a scalable and practical blueprint for trustworthy FL in adversarial and resource-constrained environments. • A novel FL framework combines SMPC, commitments, and zero-knowledge proofs. • Ensures submitted model updates match validated ones without revealing them. • Uses dynamic validation for secure and fair client selection. • Tolerates client dropouts using a threshold-based aggregation mechanism. • Outperforms baseline FL methods under adversarial and non-IID conditions.

Iqra Nazir, Nermish Mushtaq, Waqas Amin

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations.

Inbamalar T M, Abarna S, Abinaya G, D. R. · 5 authors

A decentralized Non-Fungible Token (NFT) marketplace website powered by block chain technology enables secure and transparent trading of digital assets has been proposed. Unlike traditional platforms, this system eliminates the central authority and provides full control over their NFTs to the users. It incorporates smart contracts to automate transactions, ensuring efficiency and security. As there is no limit to crypto currency, the marketplace works exclusively through crypto currencies, enhancing global access and minimizing transaction fees. The platform also features a bargaining system, allowing buyers and sellers to negotiate prices directly. By utilizing decentralized storage solutions like IPFS, the project ensures secure, immutable storage of NFT metadata and assets. This website enables users to buy or sell their NFTs. The process involves selecting the desired NFT from the list of NFTs. The next step is the "make offer" system, where the buyer and seller negotiate the price. After that, the price is fixed, and the transaction is processed with the crypto currency in their wallet. Finally, the ownership is updated by the smart contract on the block chain and the database. This website sets a new standard for efficient, secure, and innovative NFT trading. Thus, it motivates people to buy assets from the internet as they have ownership over their assets

L. K. Bang, P. H. T. Trung, Nguyen D. P. Trong, K. T. N. Ngan

No abstract is available for this record.

Mohammed R. M. Salem, Shahida Shahimi

Abstract This study provides a comprehensive bibliometric analysis of FinTech research spanning from 1968 to 2025, using 2760 articles indexed in the Web of Science database. It aims to uncover major publication trends, core theoretical frameworks, emerging topics, and the intellectual structure of FinTech scholarship. Employing VOSviewer and Harzing’s Publish or Perish software, this study maps co-occurrence networks, citation structures, and thematic clusters. It analyzes document types, source distribution, geographical contributions, keyword evolution, and the top 10 most cited papers in FinTech literature. The analysis reveals a significant surge in FinTech research since 1968, driven by the growing impact of digital finance innovations. The top three countries contributing to FinTech publications are the USA, England, and China. Dominant publication outlets include the International Journal of Bank Marketing and the Journal of Financial Services Marketing. Key research themes have evolved across three distinct periods: early banking and innovation (1968–1999), customer satisfaction and trust (2000–2011), and bank performance and digital adoption (2012–2025). Emerging topics include blockchain, mobile banking, crowdfunding, and Internet banking. The Technology Acceptance Model (TAM), along with its extended versions (TAM2, TAM3, UTAUT), is identified as the foundational theoretical framework in this field. The co-citation and keyword cluster analysis confirm the centrality of trust, risk, satisfaction, and performance in shaping FinTech outcomes. These findings not only synthesize FinTech’s academic development but also inform future research by identifying intellectual gaps and high-impact trends. The study highlights the growing integration between FinTech and consumer behavior and calls for deeper exploration into regulatory, ethical, and cybersecurity issues affecting FinTech adoption. Beyond the banking sector, the thematic patterns uncovered particularly in areas such as blockchain-based supply chain finance, crowdfunding ecosystems, and AI-enabled embedded financial services signal substantial strategic implications for non-financial firms. These include enhanced liquidity management, decentralized capital access, and data-driven business model innovation across diverse industries such as manufacturing, retail, and digital commerce.

L. K. Bang, P. H. T. Trung, N. Ð. P. Trong, K. T. N. Ngan

No abstract is available for this record.

L. K. Bang, P. H. T. Trung, N. Ð. P. Trong, K. T. N. Ngan

No abstract is available for this record.

Zeqin Liao, Yuhong Nan, Zixu Gao, Henglong Liang · 7 authors

Code reuse is a common practice in software engineering. Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract. Indeed, prior empirical studies have identified a number of issues caused by code reuse in smart contracts. Unfortunately, automatically detecting such issues poses several unique challenges. Particularly, in most cases, smart contracts are compiled as bytecode, whose class-level information (e.g., inheritance, virtual function table), and even semantics (e.g., control flow and data flow) are fully obscured as a single smart contract after compilation. Therefore, it is rather difficult to identify the reused parts of subcontract from a given smart contract, not to mention finding potential vulnerabilities caused by subcontract misuse.In this paper, we propose Satellite, a new bytecode-level static analysis framework for subcontract misuse vulnerability (SMV) detection in smart contracts. Satellite incorporates a series of novel designs to enhance its overall effectiveness.. Particularly, Satellite utilizes a transfer learning method to recover the inherited methods, which are critical for identifying subcontract reuse in smart contracts. Further, Satellite extracts a set of fine-grained method-level features and performs a method-level comparison, for identifying the reuse part of subcontract in smart contracts. Finally, Satellite summarizes a set of SMV indicators according to their types, and hence effectively identifies SMVs. To evaluate Satellite, we construct a dataset consisting of 58 SMVs derived from real-world attacks and collect additional 56 SMV patterns from SOTA studies. Experiment results indicate that Satellite exhibits good performance in identifying SMV, with a precision rate of 84.68% and a recall rate of 92.11%. In addition, Satellite successfully identifies 14 new/unknown SMV over 10,011 realworld smart contracts, affecting a total amount of digital assets worth 201,358 USD.

Ramadan Abdunabi, Md Al Amin, Rejina Basnet

Abstract Health Care Information Systems leverage Body Area Networks (BANs) to provide real-time monitoring and automated medical interventions, significantly enhancing patient care. However, security and privacy concerns present significant barriers to widespread adoption, with broken access control being a considerable risk. This research proposes an authorization framework to secure BANs, addressing critical issues such as unauthorized access and policy enforcement failures in electronic health records (EHRs). Our study introduces a Multi-Modular System Architecture that enhances access control, incorporating a Spatio-Temporal Attribute-Based Access Control (STABAC) model to enforce dynamic location and time constraints for secure data access. We introduce the Spatio-Temporal Zone (STZone) concept, simplifying policy enforcement by integrating time and location attributes. To ensure policy integrity and security, we employ Time Colored Petri Nets (TCPN) for formal policy analysis, detecting violations, and ensuring compliance with real-time constraints. Additionally, blockchain technology is leveraged to maintain policy integrity, preventing unauthorized modifications. Experimental validation demonstrates the effectiveness of the proposed framework in enforcing secure access control while maintaining system usability. The findings highlight the framework’s potential in securing BANs, offering a scalable and adaptable approach to mitigating emerging security threats in healthcare information systems.