Sheng Zhang, Tan Kia Quang, Shen Wang, Shengchen Duan · 6 authors
Scam contracts on Ethereum have rapidly evolved alongside the rise of DeFi and NFT ecosystems, utilizing increasingly complex code obfuscation techniques to avoid early detection. This paper systematically investigates how obfuscation amplifies the financial risks of fraudulent contracts and undermines existing auditing tools. We propose a transfer-centric obfuscation taxonomy, distilling seven key features, and introduce ObfProbe, a framework that performs bytecode-level smart contract analysis to uncover obfuscation techniques and quantify obfuscation complexity via Z-score ranking. In a large-scale study of 1.03 million Ethereum contracts, we isolate over 3 000 highly obfuscated contracts and identify two scam archetypes, three high-risk contract categories, and MEV bots that employ a variety of obfuscation maneuvers such as inline assembly, dead code insertion, and deep function splitting. We further show that obfuscation substantially increases both the scale of financial damage and the time until detection. Finally, we evaluate SourceP, a state-of-the-art Ponzi detection tool, on obfuscated versus non-obfuscated samples and observe its accuracy drop from approximately 80 percent to approximately 12 percent in real-world scenarios. These findings highlight the urgent need for enhanced anti-obfuscation analysis techniques and broader community collaboration to stem the proliferation of scam contracts in the expanding DeFi ecosystem.
This study assesses public trust in Bahrain regarding the potential of artificial intelligence (AI) to mitigate the use of cryptocurrencies in financial fraud and terrorist financing. The increasing risks associated with illicit financial activities have been exacerbated by the rapid expansion of e-commerce linked to cryptocurrencies, leading to vulnerabilities in financial technology systems. AI presents a viable solution for detecting, analyzing, and assessing the risks associated with cryptocurrency transactions, strengthening confidence in financial institutions’ regulatory measures. Evaluating public trust is crucial to understanding societal awareness of AI’s role in monitoring and regulating virtual financial transactions to prevent fraud. This research employs a quantitative approach to examine the key factors that enhance confidence in AI-driven auditing and oversight of cryptocurrency transfers. The findings indicate that, while AI offers significant advantages in combating financial crime, certain challenges remain. These include technological complexities, difficulties in accurately identifying users, and weaknesses in electronic financial and legal regulatory frameworks. Such challenges may undermine public trust in AI’s effectiveness in financial oversight. Addressing these concerns is essential to ensuring the successful integration of AI in financial regulation and reinforcing its role in enhancing security and transparency in cryptocurrency transactions.
Blockchain technology is increasingly recognized for its ability to provide robust security in digital systems, offering transparent, immutable, and decentralized solutions. Within the current blockchain framework, Non-Fungible Tokens (NFTs) are utilized primarily for representing unique digital assets in transactions. However, NFTs face limitations in financial contexts, particularly as they are unsuitable for use as collateral. This is due to their nonfungible nature, which makes them difficult to value consistently and exchange uniformly compared to traditional assets. To overcome these challenges, the proposed system aims to innovate by integrating NFTs into the banking sector. This system enhances the utility of NFTs by employing them in secure financial transactions, leveraging the Proof of Stake (PoS) algorithm to record each transaction on the blockchain. PoS provides a more energy-efficient and scalable solution compared to Proof of Work (PoW), making it well-suited for the high demands of financial systems. By implementing NFTs within this framework, the proposed system not only secures transactions but also expands the potential applications of NFTs beyond digital collectibles. This integration could revolutionize the way digital assets are handled in banking, offering new opportunities for financial innovation and asset management.
Xenia Hofmeier, Andrea Raguso, Ralf Sasse, Dennis Jackson · 5 authors
Aggregate signatures are digital signatures that compress multiple signatures from different parties into a single signature, thereby reducing storage and bandwidth requirements. BLS aggregate signatures are a popular kind of aggregate signature, deployed by Ethereum, Dfinity, and Cloudflare amongst others, currently undergoing standardization at the IETF. However, BLS aggregate signatures are difficult to use correctly, with nuanced requirements that must be carefully handled by protocol developers. In this work, we design the first models of aggregate signatures that enable formal verification tools, such as Tamarin and ProVerif, to be applied to protocols using these signatures. We introduce general models that are based on the cryptographic security definition of generic aggregate signatures, allowing the attacker to exploit protocols where the security requirements are not satisfied. We also introduce a second family of models formalizing BLS aggregate signatures in particular. We demonstrate our approach's practical relevance by modelling and analyzing in Tamarin a device attestation protocol called SANA. Despite SANA's claimed correctness proof, with Tamarin we uncover undocumented assumptions that, when omitted, lead to attacks.
Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contracts enable trustless interactions without the need for intermediaries, as they operate based on predefined rules encoded on the blockchain. However, smart contacts face significant challenges in cross-contract communication and information sharing, making it difficult to establish seamless connectivity and collaboration among CAVs with Web 3.0. In this paper, we propose DeFeed , a novel secure protocol that incorporates various gas-saving functions for CAVs, originated from in-depth research into the interaction among smart contracts for decentralized cross-contract data feed in Web 3.0. DeFeed allows smart contracts to obtain information from other contracts efficiently in a single click, without complicated operations. We judiciously design and complete various functions with DeFeed , including a pool function and a cache function for gas optimization, a subscribe function for facilitating data access, and an update function for the future iteration of our protocol. Tailored for CAVs with Web 3.0 use cases, DeFeed enables efficient data feed between smart contracts underpinning decentralized applications and vehicle coordination. Implemented and tested on the Ethereum official test network, DeFeed demonstrates significant improvements in contract interaction efficiency, reducing computational complexity and gas costs. Our solution represents a critical step towards seamless, decentralized communication in Web 3.0 ecosystems.
Ahmad M. Almasabi, Ahmad B. Alkhodre, Maher Khemakhem, Fathy Eassa · 6 authors
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.
The pharma supply chain has various issues, including counterfeiting, visibility, and product authenticity in the market. Some of the issues threatening the safety of the patients and the efficacy of the industry include those named above. Such issues are overcome by new technologies like Blockchain and Artificial Intelligence (AI) that are being offered. Blockchain enhances transparency since it is a distributed ledger technology that traces the drug through the supply chain and minimizes the risk of counterfeit drugs. In contrast, there is AI that extends beyond data acquisition and analytics to forecast supply chain disruptions, inventory controls, and even drug shortages to improve decision-making and performance. This paper seeks to focus on the issues that are overcome by Blockchain when integrated with Business Intelligence (BI) systems, such as drug authenticity and counterfeiting issues. This highlights the emphasis that there is a need to consider technology integration when there is a need to make a change, security considerations, and the integration of AI to make the supply chain in the pharma industries more efficient and reliable.
Sathya D, S Veena, Sangamesh Ramesh Yankanchi, Soujanya Manasa
The Internet of Medical Things (IoMT), also known as healthcare IoT, consists of interconnected medical devices and applications that enable remote monitoring of patients with chronic conditions. In existing healthcare systems, data from IoMT devices is stored in the cloud for analysis. However, major challenges include ensuring data privacy and prioritising critical health information. Rapid processing and transmission of emergency health data to hospitals are crucial for timely care, while strict privacy measures are necessary to prevent risks like data breaches, fraud, and unauthorised access to medical services. To overcome these challenges, the proposed system implements Ethereum blockchain technology and an edge AI classification algorithm on data collected in real-time. Edge computing enables instant analysis, classification, and prioritisation of health data, minimising latency and facilitating quick decision-making. Simultaneously, blockchain technology ensures robust data privacy through a secure access control mechanism. Patient information is securely stored on the blockchain and accessed via an Aadhaar card number and unique tokens. These tokens enable role-based access control, allowing authorised individuals— like doctors, nurses, patients, and relatives—to view, update, or delete specific records as needed.
Marco A. C. da Silva, Luis Hideo Vasconcelos Nakamura, Geraldo P. Rocha Filho, Luís Veiga · 5 authors
With the advancement of technologies for data registration in distributed networks, the concern of users and developers of computerized solutions with the privacy of sensitive data has increased. Thus, this work addresses a conceptual solution for an ontology-based framework so that any entity willing to provide a service using Distributed Ledger Technology (DLT) networks can model the set of privacy attributes of its system according to the business rules of its service. The solution proposed in this work encompasses the development of an architecture aimed at providing computational support for the privacy design of the actors involved in the offering and consumption of services implemented in DLTs. The architecture also includes a framework called ONSPRIDE, which uses previously stored domain ontologies to translate business rules into requirements and privacy. We conducted a proof of context by comparing the performance of two Hyperledger Fabric networks. For this purpose, we conducted a controlled experiment in which both networks operate a smart contract that manages attendance records for outdoor events. The main difference between the networks is that one uses a Certificate Authority (CA) to issue access certificates, while the other issues certificates manually. We compared the results obtained through the reports generated by the Hyperledger Caliper tool. In addition, the performance of the initialization and connection of agents in a Self-Sovereign Identity system was measured. The results of this study provide valuable insight that can help developers choose the most suitable ledger type for their Hyperledger projects and support decision-making regarding adopting a Self-Sovereign Identity system.
This article examines the evolving landscape of blockchain technology as a security framework across diverse domains. Blockchain's fundamental architecture—based on decentralization, immutability, transparency, and cryptographic security—offers distinctive advantages in addressing contemporary cybersecurity challenges. The article explores established implementations and cutting-edge innovations in blockchain security, including decentralized identity management, supply chain integrity verification, quantum-resistant cryptography, cross-chain interoperability protocols, and integration with artificial intelligence. Zero-knowledge proofs and other privacy-enhancing techniques are evaluated for their contribution to confidential yet verifiable transactions. Throughout these applications, blockchain demonstrates its capacity to create resilient systems that establish trust without centralized authorities, maintain data integrity in adversarial environments, and adapt to emerging threats. By shifting security paradigms from centralized to distributed models, blockchain technology presents transformative solutions to persistent vulnerabilities in digital infrastructure.
The rise of Distributed Ledger Technology (DLT) is revolutionizing financial systems, introducing innovations such as programmable payments, and allowing Machine-to-Machine (M2M) payments, which are essential for Industry 4.0. Despite their potential, DLT-based financial systems face barriers, including operational efficiency, regulatory uncertainty, limited institutional acceptance, and challenges in integrating with conventional financial systems. Trigger solutions emerge as a promising approach to bridge these gaps by combining the programmability and immutability of DLT systems with the regulatory certainty and established trust of conventional financial systems. This work explores key requirements for trigger solutions to support interoperability between DLT-based and conventional financial systems, enabling high-frequency programmable payments and regulatory compliance for industry 4.0. We present a state channel–based trigger solution ( SCTS ) tailored to meet industry’s requirements, offering a blueprint for integrating advanced payment capabilities into conventional financial systems. SCTS leverages the concept of justified trust-building on technological advantages to enable scalable programmable payments. We find that SCTS enables businesses to adapt to the technological demands of Industry 4.0.
The inevitable advance of quantum computing presents significant challenges to current API security frameworks that predominantly rely on classical cryptographic algorithms. This article examines how emerging post-quantum cryptographic technologies are reshaping API security landscapes in preparation for a quantum-capable future. From lattice-based algorithms like CRYSTALS-Kyber and Dilithium to Quantum Key Distribution networks achieving secure communications across intercontinental distances, a new generation of security mechanisms is being developed to safeguard API infrastructures against quantum threats. Beyond defensive applications, quantum computing offers transformative capabilities for API operations, including enhanced traffic prediction through variational quantum circuits, intelligent resource allocation with unprecedented accuracy, and anomaly detection systems capable of identifying sophisticated attack patterns that evade conventional monitoring. The integration of quantum-resistant Zero-Knowledge Proofs is simultaneously enhancing authentication processes while minimizing credential exposure. Together, these innovations are creating a comprehensive framework for API security that not only protects against future quantum threats but delivers immediate benefits in performance, privacy, and operational efficiency for organizations dependent on API communications.
This study explores the evolving role of fintech regulation as a strategic tool of geopolitical influence, highlighting how states leverage digital finance, data governance and artificial intelligence to reshape global power structures. It contrasts the regulatory models of the United States, China, and the European Union, each guided by distinct imperatives of innovation, state control, and digital sovereignty. The analysis underscores the growing politicization of fintech and the intensifying race for digital supremacy among major powers. Within this context, the article engages with issues of data decentralization, cross-sectoral data-sharing, and open banking—developments that promote financial inclusion and innovation but also generate complex governance challenges. The study concludes that coordinated transnational regulatory frameworks are essential to safeguarding financial stability and mitigating systemic risks in an increasingly contested and fragmented digital financial ecosystem.
This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, automated documentation, and intelligent insight creation. Implementation strategies using Databricks platform capabilities demonstrate how organizations can balance domain autonomy with enterprise interoperability. The architecture delivers enhanced analytics through AutoML-powered data quality with generative explanations and event-driven processing that enables real-time, predictive intelligence. Together, these capabilities create a self-improving ecosystem that democratizes data access while ensuring governance, ultimately enabling organizations to move beyond traditional reporting toward autonomous, data-driven operations with cross-domain collaboration.
Che, Zheng, Taoyu Li, Meng Shen, Hanbiao Du · 5 authors
The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, which restrict their practical applicability. In this paper, we propose StealthLink, a novel framework that addresses these limitations through cross-task domain-invariant feature learning. Our key innovation lies in transferring knowledge from the well-studied domain of blockchain anomaly detection to the data-scarce task of mixing transaction tracing. Specifically, we design a MixFusion module that constructs and encodes mixing subgraphs to capture local transactional patterns, while introducing a knowledge transfer mechanism that aligns discriminative features across domains through adversarial discrepancy minimization. This dual approach enables robust feature learning under label scarcity and distribution shifts. Extensive experiments on real-world mixing transaction datasets demonstrate that StealthLink achieves state-of-the-art performance, with 96.98\% F1-score in 10-shot learning scenarios. Notably, our framework shows superior generalization capability in imbalanced data conditions than conventional supervised methods. This work establishes the first systematic approach for cross-domain knowledge transfer in blockchain forensics, providing a practical solution for combating privacy-enhanced financial crimes in decentralized ecosystems.
Rami Almatarneh, Mohammad Aljaidi, Ayoub Alsarhan, Sami Aziz Alshammari · 6 authors
The rapid evolution of Web 4.0, characterized by decentralized systems, real-time data processing, and AI-driven interfaces, presents serious security threats such as SQL injection (SQLi) attacks, adversarial model poisoning, and IoT device spoofing. This paper presents a unified AI-blockchain framework designed to address these vulnerabilities, incorporating bidirectional LSTM networks for SQLi detection, Trimmed Mean aggregation with a reputation system for model poisoning defense, and CNN-based IoT authentication anchored to a decentralized blockchain. Evaluated on the Bitcoin OTC trust network, the framework clearly shows outstanding performance, with SQLi detection achieving 96.2% accuracy (94.8% precision and 92.5% recall), far outperforming traditional rule-based systems such as Snort (82.1% accuracy). The success rate of model poisoning attacks is reduced from 78% (in the absence of defense) to just 12% through the application of Trimmed Mean aggregation and dynamic reputation scoring, while IoT spoofing detection attains a 91.3% F1-score through cosine similarity-based matching of network traffic embeddings. The blockchain layer, which uses Delegated Proof-of-Stake (DPoS) consensus, achieves 1,450 transactions per second (TPS) with a validation latency of only 220 milliseconds, ensuring efficient real-time auditability. Furthermore, user trust scores increased by 48% after implementation (4.3/5 vs. 2.9/5 before implementation), confirming the framework's practical impact. Nevertheless, some limitations still persist, such as the 15% latency overhead due to federated learning and the use of synthetic IoT data, which may limit or reduce the framework's real-world applicability. The proposed combination of AI-based adaptive threat detection and blockchain-based tamper-proof transparency will pave the way for secure, user-focused architectures in Web 4.0, providing a scalable framework to address the evolving cyber threats in decentralized environments.
The current study aims to explore the early adoption of the metaverse in the hospitality and tourism industry, with a particular focus on understanding adoption drivers for tourism providers and the anticipated benefits for end-users. Addressing the need for practical insights in a rapidly evolving digital landscape, the review identifies 33 real-world early cases of metaverse and non-fungible token (NFT) adoption, spanning from the first metaverse hotel in 2006 to ongoing digital twins of tourism destinations up to 2023. This study follows the PRISMA technique and examines early metaverse adopters at both the enterprise and destination levels, categorizing them into metaverse-based, NFT-based, and complementary-to-tourism cases. The analysis applies two theoretical frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine adoption drivers and the Technology Acceptance Model (TAM) to assess expected end-user benefits. Key findings highlight promotion, brand engagement, new revenue streams, and community building as primary motivations for adoption, while user benefits include enhanced entertainment, social interaction, improved decision-making, and immersive experiences. By bridging theory and practice, this study contributes both actionable guidance for tourism stakeholders and a theoretical foundation for future research on digital transformation in tourism.
Chrysostomos Apostolidis, Anthony Devine, Abdul Jabbar, Yaşanur Kayıkçı · 5 authors
Purpose Despite the increasing interest of organisations in integrating advanced digital technologies, many digital transformation efforts fail to deliver long-term, sustainable value to the organisations and the stakeholders. Focusing on the impact of Non-Fungible Tokens (NFTs) in the Peer-to-Peer economy, as a case of digital transformation towards Industry 4.0, this conceptual study explores the role of algorithmic and organisational trust as facilitative drivers of sustainable digital transformation. Design/methodology/approach Drawing on Lewin’s change theory model, the study reviews and considers relevant literature on digital transformation, innovation and digital business models to support the development of a conceptual framework that explains the sustainable digital transformation process and explores the role of trust. Findings Drawing on Lewin’s change theory model and technology adoption literature, we reconceptualise digital transformation as a dynamic, non-linear process and identify four key phases in the sustainable digital transformation process (Initiating, Transforming, Re-positioning and Sustaining). We then explain the important role of algorithmic and organisational trust in sustainable digital transformation. Practical implications The study highlights the importance of trust for organisations, and explains how organisations can use blockchain-based technologies as intermediaries who take on the role of trust and the impact this has on the digital transformation process. Originality/value This paper contributes to existing literature on digital transformation in several ways. First, we reconceptualise the digital transformation process as a dynamic, non-linear process and develop a conceptual model for sustainable digital transformation following a “plan-action-result-evaluation” strategic approach. Second, we argue that there is a relocation of trust, towards technology and organisations, which impacts further upstream in the digital transformation process. This paper integrates the discussion between trust (algorithmic and organisational) and digital transformation, highlighting the need for parallel processes in creating the conditions for a sustainable transformation process.
Mint-Verse is a next-generation NFT marketplace designed to provide users with a seamless and immersive experience in buying, selling, and creating non-fungible tokens. The platform leverages blockchain technology to ensure transparency, security, and authenticity of digital assets, making it a trusted and decentralized ecosystem for digital creators and collectors. It offers a range of advanced features, including a dynamic NFT slider with countdown timers, an interactive bidding system, and a secure transaction page where users can purchase NFTs and receive digital receipts. The platform supports multiple sign-up methods, allowing users to create accounts easily and manage their profiles efficiently. A dedicated wallet section enables users to track their transactions, view their NFT collections, and manage digital assets with ease. Mint-Verse also introduces Mint-gram, an Instagram-like feature where users can showcase their NFT collections, interact with others, and engage with the growing NFT community. The platform further enhances creative possibilities by providing an NFT generation tool, allowing users to mint their own NFTs with customized attributes, ensuring flexibility and creative freedom. User experience is a core focus of Mint-Verse, incorporating an intuitive and aesthetically pleasing interface with animated loaders, a dynamic mouse cursor, interactive buttons, and real-time updates for a smooth and engaging browsing experience. Additional features such as a like button for NFTs, a contact section with email functionality, and a logout option contribute to a seamless and user-friendly navigation system. Built using the MERN stack, including MongoDB, Express.js, React.js, and Node.js, Mint-Verse ensures high performance, scalability, and efficiency. By integrating cutting-edge blockchain solutions, the platform aims to bridge the gap between artists, collectors, and investors by providing a decentralized, feature-rich, and user-friendly NFT marketplace. Mint-Verse envisions a future-proof digital ecosystem that empowers users to securely trade, create, and collect NFTs while embracing the evolving Web3 landscape, setting new standards for innovation in the digital asset industry.
Michael D. Norman, Simon Brown, Mallesh M. Pai, Laurence Smith
This paper presents a novel staking coopetition design aimed at incentivizing decentralization and continuous growth of economic security within a proof-of-stake system. Staking rewards follow a nonlinear mapping relative to stake size. This affords the highest effective yields to smaller operators, fueling network growth and giving users an incentive to delegate their stake to smaller operators. This prevents the preferential accrual and centralization of stake seen in popular blockchains such as Ethereum, where popular liquid staking protocols control large fractions of the total stake thereby having outsized potential impacts on the economic security of the protocol. The proposed system addresses key challenges such as Sybil attacks and offers a comprehensive framework for future research and implementation. We introduce innovative mechanisms and gamification elements, to enhance user engagement and provide transparency in emissions.
Decentralized trust frameworks represent a fundamental transformation in cross-enterprise integration, addressing longstanding challenges in business-to-business interactions. These frameworks leverage Web3 technologies, specifically, Distributed Ledger Technology, Decentralized Identifiers, and Verifiable Credentials to establish inherent trust between organizations without relying on centralized intermediaries. Through cryptographic verification mechanisms, organizations gain enhanced security, verifiable data provenance, reduced reconciliation overhead, and improved operational resilience. The architectural components include a decentralized identity layer providing 99.98% authentication accuracy, credential exchange mechanisms enabling selective disclosure with 99.87% privacy preservation, shared ledger infrastructure ensuring immutable audit trails, and enterprise integration components bridging with existing systems. Implementation patterns such as credential-based API authorization, event-triggered credential issuance, ledger-anchored business processes, and credential-based data exchange deliver substantial improvements in security posture and operational efficiency. Despite significant benefits including 87.3% security enhancement and 73.4% reduction in reconciliation efforts, adoption challenges remain around technical complexity, standards maturity, legacy system integration, and governance frameworks. By addressing these challenges through phased implementation focusing on high-value integration points, organizations can gradually transform their integration landscape toward more secure, transparent, and resilient models that fundamentally change how trust is established in digital business ecosystems.
Blockchain technology offers transformative potential for supply chain management by improving transparency, efficiency, and security. This paper proposes a framework that integrates blockchain with quality assurance, utilizing the Advanced Encryption Standard (AES) algorithm for data encryption, the Ethereum blockchain for decentralized architecture, and smart contracts for automation. Sales order data extracted from Walmart’s transactional database is encrypted using AES to secure sensitive information (e.g., client names, geographical data), then managed via Ethereum smart contracts that automate transactions, encryption/decryption, access control, and quality checks. The system leverages Ethereum’s peer-to-peer network for data validation and integrity. Computational experiments show AES achieves encryption and decryption times of 2.8 s and 3.2 s, respectively, outperforming RSA (6.7 s/7.3 s) and ABE (7.5 s/5.2 s) in efficiency and memory usage (0.0088 MB vs. 0.186 MB for RSA). Quality assurance metrics include 100% transaction traceability, 95% accuracy in automated quality checks, and 90% supplier compliance, surpassing traditional methods. This framework enhances operational efficiency, data security, and supply chain integrity, offering a scalable solution for Asset Management (AM), Enterprise Asset Management (EAM), and Supply Chain Management (SCM).
Abstract— This study integrates blockchain technology and machine learning to enhance credit card fraud detection. Precise fraud prediction is performed using advanced algorithms such as Random Forest, Logistic Regression, XGBoost, and Bayesian models. Tools such as Ganache and MetaMask from Ethereum blockchain facilitate safe and transparent tracking of suspicious transactions. Decentralized and tamper-proof properties of blockchain add reliability, and machine learning adds precision and flexibility. The system is highly accurate and transparent and has the potential to be used to fight financial fraud. Keywords— Credit Card Fraud, Blockchain, Machine Learning, Ethereum, Web3, SMOTE, XGBoost, Streamlit
The recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs). These tamper-resistant trustless substrates allow agents to achieve self-sovereignty through ownership of cryptowallet private keys and control of digital assets and social media accounts. DeAgents eliminate centralized control and reduce human intervention, addressing key trust concerns inherent in centralized AI systems. This contributes to social computing by enabling new human cooperative paradigm "intelligence as commons." However, given ongoing challenges in LLM reliability such as hallucinations, this creates paradoxical tension between trustlessness and unreliable autonomy. This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas. The findings will guide future DeAgents system and protocol design and inform discussions about governance in sociotechnical AI systems in the future agentic web.