The growing demand for verifiable and tamper-resistant academic achievements highlights critical limitations in traditional research credentialing systems, particularly in areas of data integrity, privacy, and traceability. Research proposes a blockchain-based certification and smart contract traceability framework that transforms research outputs such as publications, datasets, and intellectual property into immutable and auditable digital credentials. The system incorporates an encryption architecture known as the Secure Dual-Layer Advanced Triple Cryptographic Shield System (SDL-ATCSS), which combines dual-layer hybrid cryptography with a triple-stage obfuscation mechanism. To ensure data integrity and tamper resistance, the framework utilizes the SHA-256 hashing algorithm, generating cryptographic hashes of each encrypted artifact before committing it to the blockchain. The framework leverages the Ethereum blockchain and smart contracts to manage access control, version tracking, and automated certification workflows. Each research artifact is encrypted using SDL-ATCSS, hashed with SHA-256, and recorded on-chain with a timestamp and a digital signature. A randomized threshold voting protocol, supported by off-chain verification scripts, enables decentralized validation of each certification request and enforces integrity through deposit-based incentive and penalty mechanisms. A case study on academic publication certification demonstrates the practical application of the system. Research simulates the lifecycle of a research article from submission to peer review and publication, with each stage immutably recorded and verified on the blockchain. Results show that the system maintains low certification latency, strong Hash Verification, validator participation rate, tamper detection rate, lifecycle coverage, and high IPFS Pointer Validity, all of which are above 97%. Research establishes a secure, scalable, and decentralized framework for the transformation of research achievements into verifiable digital credentials, offering a transparent alternative to traditional academic validation systems.
Smart contracts have emerged as a core innovation within blockchain technology, enabling automated, trustless transactions without the need for intermediaries. While they offer a secure and transparent means of transferring assets and enforcing agreements, smart contracts are still software, and thus susceptible to bugs and vulnerabilities. Given their role in managing high-value digital assets, these flaws have been exploited to devastating effect, resulting in the loss or theft of billions of dollars. Vulnerabilities can lead to serious consequences, including unauthorized fund transfers, permanent loss or locking of assets, and the circumvention of contract logic. Critically, once deployed to the blockchain, smart contracts are immutable. Machine learning solutions in this field suffer from the lack of real accurate datasets to develop models that surpass static analyzers. In this paper we provide a methodological approach to create a curated vulnerable smart contract dataset leveraging open-source tools. A weighted ensemble mechanism is used to label contracts. Secondly, dataset preprocessing is demonstrated to create a classification friendly dataset. Finally, we train several machine learning models on a generated dataset for Solidity showing its effectiveness and strong performance in vulnerability detection.
This paper investigates performance bottlenecks of consortium blockchains under high-throughput and low-latency requirements, focusing on excessive storage burden on full nodes and redundant computation in transaction validation. Based on consortium blockchain, a novel architecture named Server-Side Core Chain (SSC) is proposed. In this architecture, the core functions of blockchain ledger data storage and smart contract execution are delegated from decentralized consensus nodes to a server cluster jointly managed and trusted by consortium members. The consensus node layer is restructured into a lightweight ``Consensus and Audit Network,â dedicated to transaction ordering and state commitment verification. This paper elaborates on the design principles, operational workflow, and security model of the SSC architecture. Theoretical analysis and prototype experiments demonstrate that the architecture significantly enhances the transaction processing capacity of consortium blockchains (experimental results show a throughput improvement of more than 18 times), greatly reduces the entry barriers and operational costs for member nodes (storage overhead reduced by over 99%), and ensures the verifiability of off-chain computations and data privacy through cryptographic commitments and zero-knowledge proofs [1]. The SSC architecture offers a new solution for deploying consortium blockchains in large-scale applications, including finance, supply chain management, and e-government.
The Blockchain is an emerging technology that is used in various applications for data security and trustworthiness. In the case of a public Blockchain, the data cannot be edited or deleted. In the case of a consortium and private Blockchain, the data can be edited or deleted based on the assigned permission, and the data privacy can be maintained. Blockchain's smart contract provides security to stored data, but it is vulnerable to various security threats. Smart contracts still suffer from different variabilities like distributed denial of service attacks (DDoS), 51% vulnerability attacks, double-spending problems, and mining Pool attacks. The smart contract, run on a Blockchain framework, is the logical contract between two or more anonymous people without involving a third party. Hyperledger and Ethereum are two important frameworks that support the development of smart contracts using Blockchain technology. This paper has tried to analyze the security issues of smart contracts developed on the Ethereum framework. An application of class scheduling management and student attendance management has been designed to validate and generate a smart contract. Received: 31 May 2025 | Revised: 4 August 2025 | Accepted: 29 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data used in this article are virtual data to implement and to establish the algorithm. It is available in GitHub at https://github.com/ashisgitup/e-learning-Blockchain.git. Author Contribution Statement Ashis Kumar Samanta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing â original draft, Writing â review & editing, Visualization, Project administration.
The Blockchain-Based Medical Insurance Transaction System (BMIT) developed in this study addresses key issues in traditional medical insuranceâinformation silos, data tampering, and privacy breachesâthrough innovative blockchain architectural design and technical infrastructure reconstruction. Built on a consortium blockchain architecture with FISCO BCOS (Financial Blockchain Shenzhen Consortium Blockchain Open Source Platform) as the underlying platform, the system leverages FISCO BCOSâs distributed ledger, granular access control, and efficient consensus algorithms to enable multi-stakeholder on-chain collaboration. Four node roles and data protocols are defined: hospitals (on-chain data providers) generate 3D coordinate hashes of medical data via an algorithmically enhanced Bloom Filter for on-chain certification; patients control data access via blockchain private keys and unique parameters; insurance companies verify eligibility/claims using on-chain Bloom filters; the blockchain network stores encrypted key data (public keys, Bloom filter coordinates, and timestamps) to ensure immutability and traceability. A 3D-enhanced Bloom filterâtailored for on-chain use with user-specific hash functions and key controlâstores only 3D coordinates (not raw data), cutting storage costs for 100 records to 1.27 KB and reducing the error rate to near zero (1.77% lower than traditional schemes for 10,000 entries). Three core smart contracts (identity registration, medical information certification, and automated verification) enable the automation of on-chain processes. Performance tests conducted on a 4-node consortium chain indicate a transaction throughput of 736 TPS (Transactions Per Second) and a per-operation latency of 181.7 ms, which meets the requirements of large-scale commercial applications. BMITâs three-layer design (âunderlying blockchain + enhanced Bloom filter + smart contractsâ) delivers a balanced, efficient blockchain medical insurance prototype, offering a reusable technical framework for industry digital transformation.
Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.
R. Suganya, P. J. Sidharth, C. Vinston Jose, P. R. Lighittha · 6 authors
This chapter explores the integration of blockchain technology and intelligent automation to address long-standing challenges in the global blood supply chain (BSC). The BSC is frequently plagued by fragmentation, traceability gaps, logistical inefficiencies, and compliance hurdles, all of which compromise patient safety and operational efficiency. By combining permissioned blockchains with smart contracts, Internet of Things (IoT) sensors, federated learning, and zero-knowledge proofs (ZKPs), this chapter proposes a modular architectureâBloodChainâthat enhances transparency, automation, and data privacy across the supply lifecycle. Experimental implementations demonstrate improved traceability, predictive analytics, and compliance with privacy regulations such as GDPR and HIPAA. The chapter concludes with a discussion on interoperability, governance, and future research directions, offering a blueprint for secure, scalable, and intelligent blood logistics in digital healthcare ecosystems.
Abstract New technologies, such as 3D digital rendering, immersive platforms, non-fungible tokens (NFTs) and AI, are creating new opportunities within the fashion industry, including virtual fashion. This chapter discusses how fashion brands communicate virtual products, by analysing the Neo-Ex campaign from Carlings. The study takes its point of departure in the key questions: What arguments are employed to market the garments, and how do fashion brands create a demand for these novel products, as well as attempting to construct a consumer understanding of what this new phenomenon is. Using a multimodal method and a document study, the study analyses campaign material, films, images, and articles, through the lens of brands as cultural intermediaries. Three themes emerged: virtual fashion as a concept for identity construction that advocates creativity , attitude , futurism , and early adopters , as well as communicating that virtual fashion is sustainability conscious. The main contribution is identifying the following three tactics for communicating virtual fashion: utilising emerging technologies for identity construction; educating the consumer; and using sustainability to validating its existence . While the first two tactics focus on building consumer awareness and acceptance, the third highlights sustainability as a rationale to legitimise virtual products.
Rashed Alnuman, Tayyab Sajid, Wesam Almobaideen, Qusai Hasan
Abstract Blockchain technology has revolutionized digital financial transactions and asset ownership by enabling decentralized and automated operations through smart contracts. Solidity smart contracts, used in the Ethereum blockchain network, facilitate secure and trustless execution of agreements. However, like any code, smart contracts are prone to vulnerabilities. Considering the assets and value of currency these smart contracts handle, their exploitation leads to severe financial losses and loss of operations. Such exploits have resulted in billions of dollars in stolen or locked assets. In this paper, we present an ensemble multilabel classifier model approach for the automated detection of vulnerabilities in Solidity smart contracts using a real smart contract dataset, with a detailed methodological process that includes processing the dataset. The proposed model stack achieves excellent results with F1 scores ranging from 82.0% to 99.9% for each vulnerability dataset. The proposed model is also compared with common static analyzer tools and models proposed in the literature following a similar approach. Moreover, we package the models into a web application, demonstrating deployment and functionality.
Machine Learning (ML) models are increasingly deployed to detect fraudulent activities in Ethereum, where phishing and scamming attacks pose serious security risks. Despite their promise, these models remain susceptible to adversarial manipulations. In this article, we present a comprehensive evaluation of ML-based Ethereum phishing detectors under a spectrum of adversarial perturbations. Our study examines multiple classifiers, including Random Forest, Decision Tree, K-Nearest Neighbors, Graph Neural Networks, and XGBoost, against rule-based, gradient-based, and black-box adversarial attacks. We conduct detailed feature-level analyses to identify transaction attributes most vulnerable to manipulation, and we evaluate the comparative robustness of classifiers under both targeted and untargeted attack scenarios. To strengthen model resilience, we assess mitigation techniques such as adversarial training and randomized smoothing, demonstrating their effectiveness in improving robustness without significant performance degradation.
âą Wavelet coherence reveals Bitcoinâs persistent link with climate policy uncertainty âą Green cryptos show context-dependent coherence with climate policy uncertainty âą Partial decoupling of green cryptos from Bitcoin emerges at medium-term scales âą Twofold framework uncovers time-scale responses of crypto to policy uncertainty âą Emphasizes need for stable climate regulations to curb crypto market volatility As global climate policy uncertainty (CPU) intensifies, understanding its intersection with emerging financial technologies becomes increasingly urgent. This study, therefore, investigates the dynamic relationship between CPU and the cryptocurrency market, focusing on Bitcoin and eight leading green cryptocurrencies (Algorand, Cardano, EOS, Hedera, IOTA, Nano, Stellar, and Tezos) using a wavelet coherence analysis. Specifically, the study employs a twofold framework: first, assessing the responsiveness of Bitcoin and green cryptocurrencies to climate policy uncertainty across time scales; second, examining Bitcoin's interaction with green cryptocurrencies to determine their potential stabilizing or decoupling effects amid regulatory uncertainty. The analysis spans from November 2017 to March 2025, capturing multiple phases of regulatory evolution and market transformation. The findings reveal that Bitcoin exhibits a structurally embedded and persistent coherence with CPU, especially over longer investment horizons. This persistent linkage highlights Bitcoinâs role in exacerbating regulatory volatility due to its significant environmental footprint. Conversely, green cryptocurrencies demonstrate more sporadic and context-dependent coherence, often aligning with major climate policy announcements or periods of regulatory scrutiny. While positioned as sustainable alternatives, these assets remain influenced by Bitcoinâs dominance and broader market sentiment, particularly at medium-term investment scales. The partial synchronization observed across key periods suggests an incomplete decoupling from both CPU and Bitcoin. These results highlight the importance of clear and stable climate regulations to reduce market uncertainty and support innovation in sustainable blockchain technologies.
The demand for goods transported by Cargo has existed at all predominant times. A large number of shipments are moved daily based on the demand that exists both in the local and the global market. In the current scenario of cargo shipment, the state of freight is usually monitored throughout the shipment process. This is entirely based on the simple temperature-based regulated storage system called cold-chain. Unfortunately, this temperature-based system does not entirely ensure the preservation of cargo shipments. This paper presents the design of a blockchain-powered Decentralized Application (DApp) to monitor Cargo in heavy goods vehicles. It includes implementing the Ropsten test network, which is integrated with a centralized cloud platform. Moreover, details of a complete evaluation of the architecture in terms of its working functionality were added, and its effectiveness in terms of its performance efficiency and real-time operation. To overcome these limitations, alternative solutions, including adopting Layer-2 scaling solutions such as Polygon or transitioning to Proof of Stake (PoS)-based blockchains for faster and more cost-effective transactions, are recommended. Selective use of Blockchain, where only critical violations are recorded, mitigates the issue of high transaction costs. Routine sensor data is efficiently managed using Google Firestore, ensuring optimal cost efficiency. The system currently relies on Infura for blockchain node access, which introduces external dependencies and potential points of failure. To reduce these risks, the adoption of self-hosted Ethereum nodes is recommended for enhanced control and reliability.
The safe handling of patient information is essential in the healthcare industry, particularly as the quantity and sensitivity of health records increase. Present systems frequently depend on centralized databases that are susceptible to unauthorized access and data manipulation, thereby restricting transparency and security. Furthermore, these systems lack the traceability required for efficient patient data management, making them prone to errors and inefficiencies in data processing. This paper proposes a patient data ledger built on blockchain technology, incorporating decentralized data management and secure transactions via Ethereum to enhance patient data handling. By leveraging blockchain, we achieve immutable records and ensure transparent, secure, and tamperresistant data storage. Unlike conventional systems that depend heavily on intermediaries for trust, our blockchain-based ledger minimizes human intervention and employs smart contracts to automate data security and access. Utilizing Ethereum's blockchain along with Web3 and MetaMask, the system enables secure, user-friendly access and effective data logging. This approach provides a reliable and scalable solution, ensuring real-time transparency and efficient handling of patient information while significantly enhancing security.
B A Mala, Clifford Thiyam, Deolin Avrel Saldanha, C. V. Dhatri · 5 authors
Food supply chains today face significant challenges, including inefficiencies, counterfeiting, and safety risks, largely due to inadequate traceability and transparency. This paper addresses these issues by leveraging a blockchain-based solution that utilizes Ethereum and smart contracts written in Solidity to document supply chain events-harvest, processing, shipping, and delivery-as an unalterable transaction. The proposed system was developed using a functional proof-of-concept web application in React.js and Web3.js to enable users to log in, authenticate, and access critical information such as product origin, transport conditions, and quality checks. This application fosters accountability and trust among all concerned. By conducting experimentation with the food based blockchain, this paper assesses the feasibility and explores challenges such as transaction fees, scalability, and system integration. The platform facilitates real-time information sharing, reduces reliance on intermediaries, and strengthens food safety practices. It provides all stakeholders with access to a single source of truth. In summary, this paper illustrates the pragmatic applicability of decentralized technologies in transforming supply chains.
S.Y. Wang, Laurence T. Yang, Xianjun Deng, Cannian Zou · 7 authors
As a key infrastructure for social fintech ecosystems, ethereum enables decentralized finance (DeFi) applications where security issues directly compromise ecosystem stability. Among critical security concerns, ethereum phishing scams stand as typical scams. Criminals employ distinctive fund transfer patterns (e.g., money laundering stages: placement, layering, and integration) to obscure illicit funds through long transaction paths. While graph neural networks (GNNs) dominate detection methods, they fail to model these long paths effectively. To address this, we propose the first framework to detect phishing scams through explicitly modeling fund transfer patterns. Our novel method, IMPUTATION, introduces: 1) a heuristic fund transfer path graph construction method utilizing iterative transaction pairing to capture complicated fund transfer patterns; 2) role-topology account embeddings encoding fund transfer patterns; 3) attention fusion leveraging initial transactions to suppress path noise; and 4) heterogeneous correlation graphs with weighted adjacency reconstruction modeling interpath dependencies. Extensive experiments demonstrate that IMPUTATION outperforms on all five metrics and detecting Ethereum phishing scams from fund transfer patterns is effective.
Katja Leifheit, Judith Pies, Hilde Van den Bulck, Alessandro DâArma · 8 authors
This article presents a quality assessment of Web3 coverage in mainstream media across four European countriesâGermany, Spain, the Netherlands, and the United Kingdomâas a starting point for discussing the standards of technology journalism that can meaningfully support an informed public. It begins with an overview of existing research on technology journalism and its role in fostering informed citizenry. This is followed by a mixed-methods content analysis, combining quantitative and qualitative approaches, to examine how the complex topic of Web3 is reported in the four European countries. Insights from journalists and editors contextualize the analysis. The findings highlight three key challenges across all countries: an event-driven rather than process-oriented focus, a narrow thematic scope on the financial applications of Web3 technology (cryptocurrencies), and a relatively limited diversity of sources. The article concludes by exploring how these challengesâand the broader trends in Web3 reportingâshed light on the potential and pitfalls of technology journalism in contemporary societies.
Abstract Blockchain wallet manages decentralized identities in Web3, which allows users to identify themselves in a verifiable yet anonymous manner by digital signatures. However, native wallet scheme has been proven vulnerable to Sybil attacks in practice, where adversaries can easily create large numbers of controlled wallets at a low cost to undermine Web3 applications. To this end, many solutions are proposed for mitigating Sybil attacks. Nevertheless, they typically rely on either costly specialized biometric hardware or behavior-based heuristics that can be easily bypassed. In this paper, we introduce EdenDID, the first trinity-bound identity management system that uniquely binds human user, wallet address, and physical device into a unified framework. EdenDID combines edge-based video recognition, on-chain transactional activity analysis, and device computational power verification to establish a decentralized Proof-of-Trustworthiness consensus. The system provides compliant users with a trusted, verifiable credential to prove their unique identity. We prototype low-cost Eden Devices and deploy them on a Layer-2 blockchain network. Our experiments demonstrate the low end-to-end latency and robust resistance performance to Sybil attacks. Moreover, we present a case study to show how EdenDID can mitigate virtual-hardware fraud in DePIN, in which EdenDID successfully binds the user, wallet, and device uniquely, thus preventing users from binding multiple wallets or devices.
Following the design of more efficient blockchain consensus algorithms, the execution layer has emerged as the new performance bottleneck of blockchains, especially under high contention. Current parallel execution frameworks either rely on optimistic concurrency control (OCC) or on pessimistic concurrency control (PCC), both of which see their performance decrease when workloads are highly contended, albeit for different reasons. In this work, we present NEMO, a new blockchain execution engine that combines OCC with the object data model to address this challenge. NEMO introduces four core innovations: (i) a greedy commit rule for transactions using only owned objects; (ii) refined handling of dependencies to reduce re-executions; (iii) the use of incomplete but statically derivable read/write hints to guide execution; and (iv) a priority-based scheduler that favors transactions that unblock others. Through simulated execution experiments, we demonstrate that NEMO significantly reduces redundant computation and achieves higher throughput than representative approaches. For example, with 16 workers NEMO's throughput is up to 42% higher than the one of Block-STM, the state-of-the-art OCC approach, and 61% higher than the pessimistic concurrency control baseline used.
Student retention is one of the rising problems seen in educational institutions. With the rising cost of education and issues in the education sector, such as curriculum relevance, student engagement, and rapidly changing technological advancements, ensuring the relevance of academic programs in a fast-evolving job market has created a significant concern for educational institutions. With the intent to adapt to such challenges, educational institutions are dealing with alternative solutions for education, in which micro-credentials are at the very center of this, which are short-term academic programs or standalone courses. However, one of the challenges of micro-credentials is a lack of credit transfer among institutions. With the lack of standardization of assessments among educational institutions, it is difficult to transfer micro-credentials to larger qualifications. Regarding such challenges, micro-credentials with blockchain technology can bring significant benefits. Blockchain technology offers a decentralized and immutable platform for securely storing and verifying credentials. This paper presents a prototype model for micro-credential verification. With the policies decided by the educational institution, the learner provides a micro-credential certificate to the system. Upon validation of the certificate by the verifying body, the educational institution will review the assessment criteria and provide exemptions based on the provided criteria. The prototype uses the Hyper-ledger Fabric platform and utilizes off-chain technology, which acts as a middle-man storage platform. With the combination of off-chain and on-chain technologies, congestion on the blockchain is reduced, and transaction speed is improved. In summary, this research proposes a prototype for secure micro-credential verification and a more efficient course exemption process.
Contract theory studies how a principal can incentivize agents to exert costly, unobservable effort through performance-based payments. While classical economic models provide elegant characterizations of optimal solutions, modern applications, ranging from online labor markets and healthcare to AI delegation and blockchain protocols, call for an algorithmic perspective. The challenge is no longer only which contracts induce desired behavior, but whether such contracts can be computed efficiently. This viewpoint has given rise to \emph{algorithmic contract design}, paralleling the rise of algorithmic mechanism design two decades ago. This article focuses on \emph{combinatorial contracts}, an emerging frontier within algorithmic contract design, where agents may choose among exponentially many combinations of actions, or where multiple agents must work together as a team, and the challenge lies in selecting the right composition. These models capture a wide variety of real-world contracting environments, from hospitals coordinating physicians across treatment protocols to firms hiring teams of engineers for interdependent tasks. We review three combinatorial settings: (i) a single agent choosing multiple actions, (ii) multiple agents with binary actions, and (iii) multiple agents each selecting multiple actions. For each, we highlight structural insights, algorithmic techniques, and complexity barriers. Results include tractable cases such as gross substitutes reward functions, hardness results, and approximation guarantees under value- and demand-oracle access. By charting these advances, the article maps the emerging landscape of combinatorial contract design, and highlights fundamental open questions and promising directions for future work.