Background Global supply chains are increasingly challenged by disruptions, environmental pressures, and evolving market demands, necessitating a strong digital transformation. This study explores how the integration of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) is revolutionizing supply chain management (SCM) by improving operational efficiency, transparency, resilience, and sustainability. Methods Adhering to the PRISMA framework, a systematic review of literature published between 2010 and 2024 was undertaken. Comprehensive searches were conducted in Scopus database. The collected literature was rigorously screened and analyzed using Atlas-ti software to identify recurring themes and assess the synergistic impact of AI, Blockchain, and IoT on supply chain operations. Results The review reveals that digital transformation significantly improves SCM through improved demand forecasting, optimized inventory management, and real-time decision-making capabilities. AI provides predictive insights that mitigate risks and streamline processes, Blockchain offers secure, transparent, and immutable records that improve trust and traceability, and IoT enables real-time monitoring and connectivity across the supply chain network. Despite these benefits, challenges remain, including cybersecurity vulnerabilities, interoperability with legacy systems, and the need for workforce upskilling. Conclusion The integration of AI, Blockchain, and IoT into SCM presents a compelling pathway toward creating more resilient and sustainable supply chains. The paper offers a comprehensive analysis of the benefits and challenges associated with these digital technologies and provides strategic recommendations for practitioners and policymakers to encourage a balanced, technology-driven, and sustainable supply chain ecosystem. JEL codes O33, M11, M15
Smart contract vulnerabilities have led to massive losses in digital assets. While researchers have proposed numerous detection methods utilizing static analysis, fuzzing, and deep learning, most are limited to identifying vulnerabilities within individual contracts. Consequently, these approaches fail to effectively analyze cross-contract interactions via external function calls, resulting in false negatives and positives. To address these limitations, we present VulCrosser, a deep learning method tailored for vulnerability detection in contract interaction scenarios. VulCrosser enables comprehensive risk assessment by analyzing function call chain traces. Specifically, it constructs a Cross-Contract Dependency Graph (CCDG) to effectively model inter-contract dependencies, network dynamics, and interaction semantics. It then employs a heterogeneous graph neural network with a two-level attention mechanism to extract and integrate complex features from the graph, ultimately achieving accurate risk assessment. We evaluated VulCrosser on three common vulnerabilities: reentrancy, timestamp dependency, and transaction state dependency. Experimental results show that VulCrosser outperforms all baseline methods, improving detection accuracy by 5.04%, 4.39%, and 5.09%, and $F 1$ scores by $4.93 \%, 4.60 \%$, and 4.99%, respectively.
The aim of the study is to focus on the marketing communication strategies in the banking and finance sector from past to present, and to detail the concepts of phygital banking and metaverse banking in terms of both usage and the advantages and disadvantages it brings from the perspective of industry professionals. In-depth interviews were conducted with a total of 6 expert bankers from 3 different banks, which constitute the universe of the research while providing sample criteria. The data transcripts created with participant statements were divided into six themes and forty-three sub-codes and presented to expert opinion to ensure the external control of the research. The data were subjected to content analysis using the MAXQDA 2022 qualitative analysis program. Based on the findings, answers were sought to the following questions: (1) What are the definition, scope, and application areas of digital marketing communication in the banking and finance sector? (2) What are the elements of digital marketing communication used in the banking and finance sector? (3) What are the advantages and disadvantages of the digital marketing communication era in the banking and finance sector compared to the traditional marketing communication era shopping experience? According to the data analysis results, participants define digital marketing as a new marketing strategy that enhances consumer experience by combining traditional financial services with digital technologies. In addition, digital applications in the banking and finance sector are concentrated in areas such as application processes, marketing activities, payment systems, and smart voice systems. While the most commonly used digital elements are artificial intelligence (AI) and QR code, augmented reality (AR), virtual reality (VR), and blockchain are following these digital elements. According to the research results, the prominent advantage of digital marketing is experience-orientedness, while it is observed that digital spaces such as metaverse, with their decentralized and anonymous structure, also bring some privacy and security disadvantages. Concepts such as digital and metaverse are important innovative concepts that shape the future understanding of marketing communication. In the study, focusing on the digital marketing strategies used in the banking and finance sector, their characteristic features and technological components were evaluated from the perspective of industry professionals, and recommendations were made to the banking and finance sector based on the findings.
Keybyte Systems, Intentix Lab , Melbourne, Australiapronab@keybytesystems.com.au Supported by AusIndustry Grant IR2405165 __________________________________AbstractModern cloud-native applications distribute business logic across multiple layers: application code, orchestration frameworks, service meshes, and infrastructure configurations. This distribution creates âhidden logicââexecution rules embedded in infrastructure that are invisible during design and difficult to trace at runtime. We present Intention Space , a computing model built on the CPUX (Common Path of Understanding and Execution) paradigm that consolidates all business logic into explicit, design-time declarations using plain-language state pulses. In our model, Design Nodes (DNs) contain computation while Gatekeepers declare execution conditions as named pulses (e.g., âpayment validatedâ: Y). The infrastructure provides only mechanical enforcement through an Intention Loop that matches runtime state to Gatekeepers without adding decision logic. We demonstrate that complex workflowsâtraditionally requiring nested if-then branching and explicit loopsâcan be expressed as linear CPUX sequences where execution paths emerge from data state rather than code branching. Our Golang implementation shows complete elimination of orchestration code while maintaining full cognitive traceability. Beyond technical innovation, CPUX addresses a critical social computing crisis: the lack of accountability in distributed social platforms. By creating unique, device-level CPUX footprints for every interaction, our model enables verifiable traceability from device identity through user intention to executed actionârestoring accountability to social computing while preserving privacy. We argue this separation of intent (CPUX) from enforcement (infrastructure) is essential for building LLM-integrated, auditable, and socially responsible distributed systems.Keywords: CPUX, Intention Space, Design Nodes, Cognitive Computing, Data-Driven Execution, Microservices Architecture, Cloud Computing, LLM Integration, Social Computing Accountability__________________________________1. Introduction1.1 The Hidden Logic ProblemConsider a typical e-commerce order processing system deployed on Kubernetes with Istio service mesh:// order-service/main.go (Business Logic Layer) func ProcessOrder(order Order) error { if order.Amount > 1000 { if err := premiumValidator.Validate(order); err != nil { return retry(premiumValidator.Validate, 3, order) } } else { standardValidator.Validate(order) } // ⊠more branching logic }# k8s/hpa.yaml (Infrastructure Layer) spec: metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 # Hidden rule: Scale when CPU > 80%# istio/retry-policy.yaml (Service Mesh Layer) spec: http: - retries: attempts: 3 perTryTimeout: 2s # Hidden rule: Retry 3 times on failureQuestion: What is the complete execution flow for a $1500 order that fails validation on first attempt?Answer: One must read and correlate:Application code (branching logic)Kubernetes manifests (scaling rules)Istio configurations (retry policies)Service mesh observability logs (runtime behavior)This hidden logic distribution creates fundamental problems:Traceability : No single artifact shows complete flowTestability : Must test infrastructure + code interactionsAuditability : Business stakeholders cannot validate logicMaintainability : Changes require coordinating multiple layersLLM Integration : No structured representation for AI reasoningSocial Accountability : Cannot trace interactions to source devices/users1.2 The Core InsightWe observe that traditional computing conflates two distinct concerns:What should happen(business intent)How to make it happen(mechanical execution)Current architectures intertwine these concerns across code, configuration, and infrastructure, making systems cognitively opaque.Our Contribution: We introduce CPUX (Common Path of Understanding and Execution) , a paradigm that separates business intent from infrastructure enforcement:CPUX Structure : Declares all possible execution paths as sequences of Design Nodes (DNs) with plain-language Gatekeeper conditionsInfrastructure : Provides mechanical execution (Intention Loop) that enforces CPUX declarations without adding decision logicDevice-Level Identity : Each CPUX execution tied to unique device fingerprint + user intention, enabling social computing accountabilityResult : Complete business logic is visible in CPUX; infrastructure remains purely mechanical; every social interaction is traceable1.3 Key ContributionsFormal Model : CPUX as cognitive execution contract with Design Nodes, Intentions, Objects, and Pulses as primitive componentsElimination of Hidden Logic : All business decisions visible in design-time CPUX declarations; infrastructure adds zero decision logicPlain-Language State Declarations : Execution conditions expressed as named pulses (e.g., âinventory confirmedâ: Y) enabling business stakeholder review and LLM integrationData-Driven Execution : Runtime branching eliminated from code; execution paths emerge from pulse state matching via SyncTestSocial Computing Accountability : Device-level CPUX fingerprints create unique, traceable identity for every social interaction, addressing the accountability crisis in platforms like Facebook, Twitter, TikTokImplementation & Evaluation : Golang framework code sample with concrete use case demonstrating zero orchestration code while maintaining full traceability1.4 Paper OrganizationSection 2 examines related work. Section 3 presents the PnR computing model and CPUX formalism. Section 4 details the architecture and implementation. Section 5 evaluates our approach through metrics and case studies. Section 6 discusses LLM integration. Section 7 introduces CPUX for social computing accountabilityâthe urgent global need. Section 8 concludes with future directions.__________________________________2. Related Work2.1 Workflow Orchestration SystemsAWS Step Functions [1] and Azure Logic Apps [2] provide visual workflow definition with explicit state machines. However, they:Use proprietary JSON/XML DSLs (not plain language)Embed conditional logic in workflow definitions (still branching)Remain platform-specific (vendor lock-in)Require reading workflow definitions to understand flowCannot trace to device/user identityApache Airflow [3] and Temporal [4] define workflows as code with DAG structures. They improve on step functions ,recoverability but:Business logic still in code (if-then branches)Workflow orchestration separate from executionNo plain-language condition declarationsNo device-level traceabilityCPUX Advantage : All logic in plain-language pulses, platform-agnostic, no explicit branching in declarations, device-level identity for every execution, recoverability built into platform. 2.2 Service Mesh & OrchestrationIstio [5] and Linkerd [6] provide traffic management, retries, circuit breaking. Kubernetes Operators [7] encode reconciliation logic. These systems:Hide business rules in YAML configurationsDistribute logic across mesh config + operator codeFocus on infrastructure concerns (not business flow)Lack unified view of complete execution pathNo user/device attributionCPUX Advantage : Consolidates all execution logic in CPUX; infrastructure config aligned with business intent; device identity integral.2.3 Event-Driven ArchitecturesApache Kafka [8], AWS EventBridge [9] enable event-driven systems with loose coupling. Reactive systems [10] promote message-passing. However:Event flows implicit (must trace message paths)Conditional logic in event handlers (code-level branching)No design-time declaration of all possible flowsNo provenance tracking to source deviceCPUX Advantage : Explicit declaration of all event-driven paths as DN sequences with visible Gatekeepers; device identity in event provenance.2.4 Intent-Based SystemsIntent-Based Networking [11] translates high-level intents to network configurations. Policy-based management [12] separates policy from mechanism. Closest to our work, but:Focus on infrastructure (not application logic)Policies often domain-specific (not general computing)Limited plain-language expressivenessNo user accountabilityCPUX Advantage : General-purpose computing model with full plain-language pulse declarations applicable to any domain; device-level user accountability.2.5 Formal Methods & Model CheckingTLA+ [13], Alloy [14], and Petri Nets [15] enable formal specification and verification. These are powerful but:Require specialized formal notation (high learning curve)Specification separate from implementation (sync problems)Not designed for runtime executionNo social computing traceabilityCPUX Advantage : Declarations are executable; CPUX structure IS the implementation contract; device identity embedded.2.6 Social Computing & AccountabilityBlockchain-based identity [16] and zero-knowledge proofs [17] address digital identity but:Focus on cryptographic primitives (not execution tracing)Donât integrate with application logicNo cognitive representation of intentFederated social networks [18] (Mastodon, ActivityPub) improve decentralization but:Still lack device-level traceabilityNo structured intent representationCannot prove user intended specific actionCPUX Advantage : First system to integrate device identity, user intention, and execution trace in single cognitive framework.2.7 PositioningCPUX is the first system to combine:Plain-language execution conditions (like Intent-Based Networking)Executable specifications (unlike formal methods)Complete flow visibility (unlike distributed orchestration)Zero hidden infrastructure logic (unique contribution)Device-level social accountability (unique contribution)__________________________________3. The PnR Computing Model3.1 Core Abstractions3.1.1 Pulse: Atomic State UnitA Pulse is the fundamental data unit representing a named state with optional response and trivalence:Pulse = (Name: String, Response: Value, Trivalence: {Y, N, U})Name : Plain-language identifier (e.g., âpayment validatedâ)Response : Optional value (e.g., transaction ID)Trivalence : Y (yes/true), N (no/false), U (undecided)
Yan Liu, Diandong Liu, Yongyi Han, Qianying Yang · 6 authors
Smart contracts are central to decentralized applications, enabling automated execution and eliminating intermediaries. However, once deployed on the blockchain, they become immutable, making them susceptible to significant security vulnerabilities that could lead to financial losses and data breaches. Despite advancements in static analysis and symbolic execution for vulnerability detection, these methods remain limited in dynamic environments and large-scale contracts. Recently, large language models (LLMs) have shown potential in code analysis and vulnerability detection, but given concerns over the security of large models, there is an urgent need for evaluations of open-source LLMs to guide their deployment and application. This study aims to assess the performance of state-of-the-art open-source LLMs in detecting smart contract vulnerabilities. Using the Smartbugs dataset, we evaluate four prominent models-OpenAIâs gpt-oss 20B and 120B, Qwen3 235B, and DeepSeek R1 671B. The study focuses on three main areas: 1) evaluating the effectiveness of these models in detecting various vulnerabilities; 2) comparing them with existing detection tools; and 3) analyzing their limitations and potential for future development. The main contributions of this study include the first comprehensive evaluation of open-source LLMs in smart contract vulnerability detection, a detailed comparison with traditional tools, and the release of open experimental methods and data to foster further research in the field. The code used in this study can be found at https://github.com/zumAndenken/Open-Source-LLMSmartContract.
The integration of decentralized security mechanisms into smart drone surveillance systems marks a transformative advancement in the field of unmanned aerial monitoring. Traditional centralized architectures are often vulnerable to single points of failure, data breaches and latency issues specifically in case of operation in hostile or remote environments. By leveraging blockchain technology, drone networks can establish a tamper-proof, distributed ledger that ensures the integrity and authenticity of surveillance data in real time. Internet of Drones is a decentralized network linking drones access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications such as package delivery, traffic surveillance and rescue including navigation services. One of the potential methods to enhance user privacy, data security and authentication, especially in peer-to-peer UAV networks is blockchain technology, which has now been gained prominence.
The protection of sensitive assets through confidentiality, integrity, and accountability in distributed computing environments is one of the most important factors to consider because the threats to cloud infrastructures keep changing. To lessen these issues, the current work proposes a consensus based confidential data orchestration model (CD-COF) that employs demoralized trust enforcement and adapt meter of encryption that is used to bolster the safety of data. As in the suggested approach, every data item will be in a dynamic encryption cycle to integrate lattice-based post-quantum cryptography with the homomorphic encryption algorithm, allowing secure data storage and computation against aspiring cryptanalytic algorithms. The require cipher fragments or data is irreversibly encoded into a distributed registry replicated by cluster of validators in such a way that they result in tamper-proof inspection without revealing the data. To regulate access control, smart contracts authenticate zero-knowledge compliance proofs to ensure that a requester who wishes to gain access without supplying credentials does it. More importantly the ephemeral session tokens generated by verifiable random functions prevent attacks of re-play and collusion and a multi-dimensional trust scoring degree that a-dynamically reallocates access rights to the user, based on his or her usage habits. With the adaptive encryption and immutable anchoring implemented and verifiable access orchestration exploited, the new solution forms a robust and resilient security model in data protection of the decentralized domain of the cloud environment in the future. In it integrated domain of encoding, anchorage with ledgers and trust-based enforcement of access controls, the proposed CD-COF method has a total accuracy of 97 percent in protecting data at the cloud based.
The fast evolution of the cryptocurrency has brought new dynamics to the trading setting at the international level which has altered the manner in which value is created, captured and retained across border. This paper will examine the impacts of cryptocurrency in the international trade by examining its impact on efficiency in transactions, financial accessibility, regulation, and market stability. The cryptocurrencies are based on decentralized blockchain networks, and they allow almost instant international payments, that do not involve the use of intermediaries and less expenditure on transactions by traders. The features are very beneficial to the firms in a region where the conventional banking systems have limited scope, which can spread to international trade. The paper also discusses the role of cryptocurrency in enhancing transparency through unchanging digital records of transactions to increase confidence between the trading partners and reduce the risks of fraud and late payments. At the same time, the insecurity of digital currencies, changing regulations, and suspicion of unlawful financial operations are not the last challenges that may limit mass adoption. The reactions of governments and international institutions to such uncertainties through new rules, cross-border banking digital currency (CBDC) programs and cross-border regulatory collaboration are discussed. With the compilation of the case studies in the various markets, the research finds out the effective implementations of cryptocurrency in trade finance, and the limitations faced by businesses, as they seek to trade volatile markets in digital currencies. According to the findings, one can state that although the role of cryptocurrency is hardly going to replace traditional financial systems wholesale, it is becoming a more and more prevalent complementary opportunity that will be able to facilitate faster payments, increased financial accessibility, and better flexible trading opportunities. Lastly, the study finds that the participation of cryptocurrency in global commerce will depend on upcoming tendencies in the harmonisation of the regulations, technological security and maturity of the industry. These will determine how the digital assets will see the dawn of day as either a trusted and stable component of the world trading system or a niche provision that is put to selective use in special industries.
Smart contracts operating on blockchain platforms like Ethereum often face critical security challenges, which can lead to significant financial damage. Existing detection techniques frequently fall short in terms of precision or scalability. To overcome these limitations, we introduce an innovative deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks and incorporates an attention mechanism. This model is designed to extract both short-range syntactic cues and long-distance contextual patterns from smart contract code. By applying advanced preprocessing and employing the SMOTE algorithm to handle class imbalance, our approach excels in identifying key vulnerabilities, including Reentrancy, Timestamp Dependency, Integer Overflows, and Dangerous Delegatecall . Experimental evaluations reveal notable gains in detecting rare vulnerability types, particularly in terms of recall and precision, underscoring the effectiveness of attention-augmented hybrid models for enhancing smart contract security analysis.
Abdul Aziz Usman Balarabe, Gilbert I.O. Aimufua, Kene Tochukwu Anyachebelu, Umar Abdullahi
The management of judicial workflow through traditional paper-based and centralized systems presents a wide range of challenges. This is particularly evident in processes such as: case filing, evidence submission, and verdict execution, where inefficiencies, bureaucratic delays, and security vulnerabilities often undermine the credibility of the judiciary. While electronic systems like Case Management Systems (CMS) and Electronic Court Filing (ECF) have been introduced to digitize court operations, they remain largely centralized and therefore susceptible to unauthorized access, data manipulation, and cyberattacks. Numerous scholars have proposed digital and automated solutions for judicial processes; however, existing models often fail to adequately address key challenges of transparency, immutability, and interoperability across jurisdictions. The main objective of this paper is to provide a blockchain-enabled smart contract framework for judicial workflow management that integrates automation, security, and transparency into legal processes. Data flow and use case diagrams were developed to illustrate the core functionalities of the framework, including case registration, evidence management, judicial rulings, and court scheduling. Access to records within the framework is governed by blockchainâs decentralized consensus mechanism, while smart contracts automate routine judicial tasks based on predefined rules. The adoption of this approach is expected to significantly reduce administrative inefficiencies, minimize fraudulent practices, and enhance trust in judicial outcomes.
Rui Wu, Guangfu Wu, Kangjun Li, Lei Liu · 6 authors
As blockchain technology matures, the application scope of smart contracts continues to expand. Security issues in smart contracts have become an important research focus. In view of the limitations of traditional vulnerability detection methods such as limited vulnerability types and high false positive rate, this paper proposes a smart contract vulnerability detection model based on deep learning. By constructing token sequence sets and data flow graphs and inputting them into the pre-training model of this article, we obtained a pre-training model optimized for Solidity code and implemented a multi-label classifier in the downstream detection layer. The proposed model can detect integer overflow vulnerabilities, transaction sequence dependency vulnerabilities, timestamp vulnerabilities, and reentrancy vulnerabilities in the target contract. Experiments show that this model is better than other detection tools in detection effect.
Rutuja Chirwatkar, Beemkumar Nagappan, L. P. Singh, Anoop Dev · 6 authors
Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.
This study examines the legal validity of contracts generated by AI-based smart contract generators from the perspective of Indonesian civil law, in comparison with French law. The background lies in the development of Society 5.0, the use of blockchain and AI in contract drafting, and the absence of specific regulations on smart contracts and generators in Indonesia. This research analyses contracts produced by smart contract generators, the legal framework governing the use of smart contract generators in contract formation in Indonesia and France, and the implications of this comparative analysis for strengthening legal certainty regarding smart contract generators in Indonesia. The study is a normative juridical research using statutory and comparative approaches, relying on primary legal materials, namely the Indonesian Civil Code, the Electronic Information and Transactions Act, the French Civil Code and AI regulations, as well as secondary materials on smart contracts, blockchain, and AI. The results show that contracts generated by such tools have the characteristics of automation, immutability, transparency, security, and decentralisation, but from a legal standpoint must still satisfy the general requirements for a valid agreement. In Indonesia, smart contracts can in principle be classified as electronic contracts insofar as they comply with Article 1320 of the French Civil Code, while the generator is viewed merely as a tool owned by the parties, with legal responsibility placed on users and a remaining regulatory gap on technical aspects. In France, smart contracts generated by AI are assessed under the general rules of contract law but are reinforced by the explicit recognition of DLT in financial law and a more developed AI supervision framework. Based on these findings, the study concludes that Indonesia needs to strengthen its national framework through explicit recognition of the contractual function of smart contracts, the adoption of technical standards and liability rules for generator providers, and the development of judicial and sectoral guidelines to ensure legal certainty and protect weaker parties.
Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious keyâvalue store encoding (OKVS) mechanism is introduced to enable secure and efficient keyâvalue mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.
The proliferation of Internet of Things (IoT) applications and on-demand logistics has fostered crowdsourced delivery systems where dynamic coordination among senders, couriers, and receivers enables efficient lastmile logistics. However, centralized dispatching exposes privacy and reliability risks, including single points of failure and leakage of routing and transaction data. Blockchainbased Payment Channel Networks (PCNs) address these limitations by moving frequent interactions off-chain while maintaining verifiable settlement on-chain. This paper presents a blockchain-anchored privacy-preserving path optimization protocol that supports variable-amount multihop payments over PCNs. By combining Pedersen commitments and lightweight zero-knowledge proofs (ZKPs), the protocol verifies transaction correctness without revealing amounts, and employs blind-channel operations to prevent intermediaries from accessing sensitive data. Experimental results show that the proposed scheme achieves strong privacy protection and scalability with low computational cost, making it suitable for blockchain-based IoT delivery environments.
ExecMesh introduces cryptographically verifiable computation as a foundational primitive for regulatory compliance and audit trail requirements in AI/ML systems [1â3]. By combining commitmentbased verification with secure multi-party oracles and a two-tier regulatory architecture, ExecMesh enables enterprises to meet FDA, SEC, and EU AI Act requirements while maintaining the benefits of decentralized infrastructure. Immediate Value Proposition: ExecMesh provides immediate value as an audit trail and provenance layer for regulated AI systems, independent of advances in zero-knowledge proof technology. Even without full verification of large neural networks, the system delivers cryptographic guarantees for data integrity, execution timestamps, and pipeline reproducibilityâmeeting core regulatory requirements today.
Traditional traceability systems in aquatic product supply chains suffer from centralization, data tampering risks, and single points of failure, which directly threaten food safety and consumer trust. To address these vulnerabilities, this paper proposes a secure and trustworthy traceability model leveraging blockchain technology. We integrate the InterPlanetary File System (IPFS) to alleviate on-chain storage pressure while ensuring data immutability through cryptographic linking. To counter the risk of plaintext exposure in IPFS, we introduce a symmetric encryption mechanism prior to off-chain storage. The proposed architecture ensures end-to-end data integrity, confidentiality, and access control. Security analysis and experimental validation confirm the robustness and feasibility of our approach against common threats such as unauthorized access and data leakage.
Non-fungible tokens (NFTs) are blockchain-based digital assets that enable verifiable ownership of unique items, ranging from digital art to virtual real estate. This paper explores the emerging role of NFTs within an innovative vision of the Metaverse, analyzing the potential to reshape asset representation, economic structures, decentralized technologies and the challenges arising from their intersection. In this paper, we analyze the foundational concepts and current research, understand their interconnectedness by going through real-world applications and deployments, and study various case studies in the domain to elucidate the subject. The discussion offers key insights into better digital asset representation, user-driven economies, and new monetization models. Additionally, we examine the critical challenges of scalability, interoperability, and sustainability introduced by these innovations. Notably, this study contributes significantly to explaining the future trajectory of NFT adoption and understanding its implications for digital innovation, social interactions, and economic structures in the Metaverse.