Financial Technology (FinTech) is reshaping the worldwide financial industry by introducing innovations like digital transactions, artificial intelligence (AI), blockchain, mobile banking, data analysis, and integrated finance. These advancements are improving the effectiveness, openness, and availability of financial services, fostering financial inclusion, and decreasing reliance on traditional banking systems. This research investigates how FinTech plays a crucial role in stimulating innovation, inclusivity, and digital change in the financial landscape. It also delves into the opportunities arising from digital financial services and the obstacles related to cybersecurity, data protection, adhering to regulations, and ethical considerations. The research is grounded in an examination of recent literature, industry studies, and policy papers to grasp present trends and future advancements in FinTech. The results indicate that FinTech has emerged as a vital facilitator of sustainable financial expansion and economic progress. The research offers valuable perspectives for scholars, decision-makers, financial organizations, and industry professionals to comprehend the direction of digital finance.
Cybersecurity is one of the most pressing concerns with regard to autonomous systems' ever-increasing adoption across multiple sectors, including transportation, health care, and smart city developments. The aim of this chapter is to focus on the various methods of securing autonomous systems through artificial intelligence (AI)-powered intrusion detection systems (IDS) and privacy-preserving mechanisms. For example, this chapter will explore the use of machine learning for anomaly detection as well as secure federated learning and blockchain technology to enhance the integrity of data in autonomous systems. Furthermore, it will provide an overview of the use of adversarially attacking AI models and provide recommendations for reducing cyber risk. Utilizing AI-based security frameworks, autonomous systems can identify threats and respond to them almost instantly, while ensuring user privacy. Finally, this chapter addresses the regulatory hurdles surrounding autonomous technology, as well as potential areas for future research related to security in autonomous systems.
The construction industry is undergoing a significant transformation with the adoption of decentralized models, which leverage distributed decision-making, collaborative networks, and advanced technologies such as blockchain, digital twins, and artificial intelligence (AI). These innovations promise enhanced transparency, efficiency, and stakeholder engagement in high- rise building projects. However, decentralization introduces unique risks—spanning technical, social, economic, legal, and environmental domains—that challenge traditional risk management frameworks. This research systematically identifies and categorizes these risks, emphasizing their implications for decentralized high-rise construction. Key technical risks include design clashes and quality inconsistencies due to fragmented workflows, while social risks encompass labor disputes and community opposition. Economic risks arise from budget fragmentation and supply chain volatility, legal risks stem from contractual ambiguities and regulatory non- compliance, and environmental risks involve waste mismanagement and increased carbon footprints. To address these challenges, the study proposes a comprehensive risk management framework integrating emerging technologies. For instance, Building Information Modeling (BIM) and digital twins enable real-time clash detection and quality assurance, blockchain ensures transparent and automated contract execution, and AI-driven analytics predict safety hazards and cost overruns. The framework is validated through a case study of Skyline Towers in Dubai, where decentralized strategies reduced design errors by 45% and payment delays by 80%. The research employs a mixed-methods approach, combining a systematic literature review with empirical analysis of real-world projects. Findings highlight the critical role of stakeholder alignment, hybrid governance models, and sustainable practices in mitigating risks. The study concludes with actionable recommendations for policymakers and industry practitioners, advocating for standardized digital protocols, adaptive risk governance, and proactive environmental controls. By bridging the gap between technological innovation and risk management, this research contributes a forward-looking framework to enhance resilience and efficiency in decentralized high-rise construction, ensuring sustainable urban development in an increasingly complex industry landscape.
The increasing adoption of cryptocurrencies has created new opportunities for digital financial innovation while simultaneously exposing individuals and institutions to sophisticated forms of financial fraud. Conventional rule-based fraud detection systems have become inadequate in addressing the dynamic and complex nature of blockchain-enabled financial crimes, leading to growing interest in the application of artificial intelligence (AI). This study systematically reviews the literature on artificial intelligence techniques for cryptocurrency fraud detection, with particular emphasis on their relevance to the Kenyan digital financial ecosystem. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Peer-reviewed studies published between 2020 and 2026 were identified from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Following the screening and eligibility assessment, 19 studies were included in the final qualitative synthesis. The findings reveal that machine learning, deep learning, hybrid AI models, and blockchain analytics significantly enhance cryptocurrency fraud detection by improving anomaly detection, transaction monitoring, predictive accuracy, and anti-money laundering compliance. Compared with traditional rule-based approaches, AI techniques provide faster, more adaptive, and scalable solutions capable of detecting evolving fraud patterns in decentralized financial systems. However, the review also identifies challenges relating to limited high-quality datasets, algorithmic bias, lack of explainability, cybersecurity risks, privacy concerns, and inadequate regulatory frameworks, particularly within developing economies. Furthermore, the review highlights a scarcity of empirical research focusing on cryptocurrency fraud detection in Kenya and identifies opportunities for developing localized datasets, explainable AI models, and context-specific regulatory frameworks. The study concludes that artificial intelligence has considerable potential to strengthen cryptocurrency fraud detection and financial security in Kenya, provided that technological, ethical, and regulatory challenges are adequately addressed. The findings provide valuable insights for researchers, financial institutions, technology developers, and policymakers seeking to enhance AI-driven fraud prevention within the country's evolving digital financial ecosystem.
E-Healthcare Systems (EHS) are transforming medical service delivery by enabling real-time data sharing, remote diagnostics, and integrated care via IoT and cloud infrastructures.However, the increasing volume of sensitive medical data being transmitted over distributed systems creates serious privacy and security concerns.This article reviews several papers on the EHS Data Privacy Framework, addressing critical issues such as illegal data access, identity exposure, and data integrity breaches.This review examines the existing data privacy frameworks used in EHS, focusing on four domains: traditional EHS, cloud-based EHS, IoT-based EHS and blockchain-based EHS with an emphasis on author, year, objective, and limitation.This framework provides a scalable and interoperable approach to protecting privacy for future healthcare systems.
As the world is witnessing the emergence of Metaverse, which is an immersive decentralised digital environment, there has been a sudden rise in unprecedented cross border economic and social activities which has facilitated transactions through the medium of virtual goods, NFTs, digital avatars and user generated content. This shift deviates from the conventional definition of Intellectual property and hence presents a notable threat in the territorial and national legal systems that is built on the roots of these grundnorm leading to substantial jurisdictional and enforcement gaps. This paper adopts a systemic literature review method by blending academic research, legal precedents, and policy documents to put forward how the core concepts of metaverse like user anonymity, decentralized blockchain structures, instantaneous duplication of digital assets, and borderless virtual economies unsettle the framework of traditional frameworks of intellectual property. With the help of comparative analysis of landmark case like Hermès International v. Rothschild, Nike v. Stock X, and Juventus F.C. v. Blockeras, the study puts forward the different challenges that the modern-day courts are facing in tackling with emerging virtual disputes while implementing the copyright and trademark doctrines. The paper further delves into the efficiency of international agreements like TRIPS and the Berne Convention in underlining the dispersion in global regulatory services. Alongside problem identification, this paper also proposes a hybrid framework that would bring together blockchain verification, cryptographic rights management, AI-based monitoring, legal harmonization with the help of model treaties and statutory reforms accompanying decentralised arbitration mechanisms. The study concludes that effective, equitable, and sustainable IP enforcement in the metaverse requires coordinated international cooperation, collaborative multi-stakeholder governance models balancing robust IP pr...
Open access
2 source records
Law, AI, and Intellectual Property
Dispute Resolution and Class Actions
Legal, Health, Environmental and COVID-19 Challenges
Cryptocurrency has emerged as one of the most significant developments to accompany the digitization of global finance, and its footprint in India has expanded rapidly despite an unsettled regulatory environment. This paper examines how Indian investors perceive the opportunities and risks associated with cryptocurrency and blockchain technology, and evaluates whether their level of awareness shapes that perception. A structured questionnaire survey was administered to 158 respondents drawn from different age groups, educational backgrounds, occupations, and income levels in Karnataka, and the resulting data were analyzed using percentage analysis, frequency distribution, and the Chi-square test of independence. The findings indicate that a large majority of respondents, particularly those aged 21-30, view cryptocurrency and blockchain as tools capable of improving transparency, financial inclusion, and entrepreneurship, while simultaneously expressing concern over price volatility, cybersecurity threats, and unclear taxation rules. The Chi-square test confirmed a statistically significant association between investor awareness and perception of cryptocurrency (calculated value 19.41 against a critical value of 9.488 at 4 degrees of freedom and the 5 percent level of significance), leading to rejection of the null hypothesis. The study concludes that a clear, balanced regulatory framework combined with investor-education initiatives would allow India to capture the innovation potential of digital assets while containing the risks associated with their adoption.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
A Decentralized Application (DApp) is a distributed, open-source software application that runs on a peer-to-peer (P2P) blockchain network. DApps are emerging as a transformative force across various sectors, leveraging blockchain technology to create applications that operate autonomously, offer enhanced security and transparency, and function without a central authority. Common security issues with DApps include smart contract and blockchain vulnerabilities, phishing and social engineering attacks, and key management challenges. Mitigation strategies involve thorough code audits, formal verification, multi-signature wallets, and robust security frameworks. This chapter provides a detailed analysis of DApp security, focusing on theoretical aspects, architectural components, and specific vulnerabilities in smart contracts, oracles, blockchain protocols, front-end interfaces, and cross-chain interoperability mechanisms.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.
Validation of a secure blockchain architecture's effectiveness and robustness may be achieved via a methodical process that involves comprehensive testing and implementation in real-world environments. The testing process includes analyzing requirements, setting up the environment, and conducting detailed evaluations. All aspects of the smart contract, from its logic and functionality to its defences against common attack vectors like Sybil and re-entrancy vulnerabilities, are evaluated in these reports. Performance testing under different loads and realistic network conditions is essential for assessing scalability, latency, and throughput, in addition to API and integration testing, which ensure that system components operate well together. System resilience to defects and hostile events is tracked, critical test cases are automated, and large-scale peer-to-peer networks are modelled to evaluate the framework's robustness further. Supply chain verification and clinical trial administration are two examples of real-world applications of blockchain technology that demonstrate its ability to secure sensitive activities on a large scale. These use cases also give light on the system's efficacy, data integrity, and anomaly detection capabilities. Verifying the scalability, security, and reliability of a blockchain architecture against real business objectives via integrated deployments and complicated testing methods is essential for a safe blockchain.
The inherent challenge of balancing scalability, security, and decentralization – commonly termed the blockchain trilemma – continues to hinder the adoption of distributed systems. This paper presents InternxtChain, a decentralized storage framework designed to address this trilemma through a novel integration of erasure-coded sharding, zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs), and a sharded Proof-of-Storage consensus mechanism. By leveraging aggregated BLS-381 signatures and distributed redundancy protocols, the framework achieves a throughput of 2,800 transactions per second with a latency of 420 milliseconds across 1,024 nodes, surpassing Filecoin by a factor of 3.5 and Ethereum’s capacity by 165 times. The system maintains 99.9% data integrity even under adversarial conditions involving 30% Byzantine nodes. Additionally, InternxtChain reduces storage costs to $0.002 per gigabyte, representing an 85% reduction compared to centralized alternatives like AWS S3. Empirical evaluations demonstrate linear scalability to 4,200 transactions per second with 2,048 nodes, alongside hardware affordability at $180 per node. These advancements not only outperform decentralized platforms in throughput by 2.8 times but also ensure GDPR-compliant data sovereignty, positioning InternxtChain as a pioneering solution for Web3 ecosystems seeking to harmonize enterprise-grade performance with decentralized trustlessness.
The digital transformation of higher education creates new opportunities to enhance the effectiveness, inclusiveness, and sustainability of dual education systems. However, empirical evidence on the integration of emerging technologies into dual education remains limited in developing and post-Soviet countries. This study investigates stakeholder perceptions of digital transformation in dual higher education in Uzbekistan and explores the potential of Artificial Intelligence (AI), Virtual Reality (VR), and blockchain technologies to support inclusive and sustainable learning environments. A convergent mixed-methods design was used. Quantitative data were collected from 312 students and 80 industry representatives through structured surveys, while qualitative data were obtained from semi-structured interviews with 24 academic staff members involved in dual education programmes. Descriptive statistics, correlation analysis, and thematic analysis were used to examine stakeholder readiness, implementation barriers, and future development priorities. The findings indicate strong support for digital transformation by stakeholders. Most students perceived dual education as more effective than traditional instruction (81%), and 74% expressed interest in AI- and VR-supported learning environments. Employers demonstrated a high readiness to adopt digital assessment tools (85%) and blockchain-based credential verification systems (80%). However, major challenges were identified, including insufficient digital infrastructure, limited funding, inadequate professional development opportunities, and regulatory uncertainty. Only 31% of students considered the existing digital infrastructure sufficient for advanced technology integration.Based on these findings, this study proposes an integrated framework that combines AI-driven personalized learning, VR-based experiential training, and blockchain-enabled credential verification within the principles of Universal Design for Learning (UDL) and Sustainable Development Goal 4 (SDG 4). The framework aims to enhance educational accessibility, strengthen industry–university collaboration, and support equitable participation in dual higher education. This study contributes empirical evidence from a developing country context and offers practical recommendations for policymakers and higher education institutions seeking to implement inclusive and sustainable digital transformation strategies in dual education systems.
The rising use of the Internet of Things (IoT) has changed the communication and automation landscape in various industries. However, the growing number of interconnected and vulnerable IoT devices has created several cybersecurity challenges, and the conventional intrusion detection system is not designed to handle the dynamicity of sophisticated cyber-attacks and secure information management. This study presents a blockchain-based security framework for intrusion detection in an IoT environment that uses a Gated Recurrent Unit (GRU) to achieve high-level detection accuracy and blockchain technology to guarantee information security. Edge-IIoTset benchmark data containing about 2.2 million traffic instances and 61 traffic features were collected, preprocessed, and split into training, validation, and testing datasets at a ratio of 70:15:15 for model development and evaluation. The GRU network was trained to capture sequential patterns in network traffic with high accuracy, while the blockchain layer was leveraged to ensure secure detection record storage and information sharing. The model attained 99.12% accuracy, 99.08% precision, 98.97% recall, 99.02% F1-score, and 0.9987 ROC-AUC. Additionally, the blockchain layer achieved an average of 850 transactions per second with a 2.3-second block confirmation time, while the framework recorded an average of 3.2 millisecond traffic detection time. Thus, the proposed framework was efficient and effective in detecting and responding to cyber-attacks in an IoT network.
Purpose The purpose of this paper is to apply the integration of signaling theory and self-congruity theory to explain the mechanism by which blockchain-enabled traceability and transparency influence consumers’ willingness to pay a premium for sustainable fashion products. Design/methodology/approach Quantitative data was collected through an online survey with 622 participants in Vietnam using snowball sampling. The participants were those who had awareness or experience with sustainable fashion and blockchain technology. The research model and hypotheses were tested using partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS 4 software. Findings The study found that blockchain signals strongly activate four types of symbolic meanings (status, environment, innovation and fashion). These symbolic meanings reinforce identification with personal identity and feelings of brand authenticity, which in turn promote willingness to pay a premium. Notably, the results showed that hyperopia did not play a moderating role in the relationships between psychological mechanisms and willingness to pay a premium. Originality/value This study contributes by extending signaling theory and self-congruity theory to a blockchain-enabled sustainable fashion context. Rather than proposing a fundamentally new psychological mechanism, it shows how blockchain-based traceability and transparency can function as credibility-enhancing signals that activate established symbolic, identity-related and authenticity-based processes associated with willingness to pay a premium.
Environmental Sustainability in Business
Consumer Behavior in Brand Consumption and Identification
A blockchain-enabled AI healthcare system is proposed to enhance disease prediction and secure healthcare data management. The framework integrates advanced artificial intelligence techniques with robust data security mechanisms to ensure accurate forecasting and safe handling of sensitive medical information. The system employs a hybrid GRU–LSTM model for real-time chronic disease prediction using electronic health records, IoT sensor outputs, and wearable device data. To further improve prediction performance, the Fireworks Algorithm is utilized for hyperparameter optimization and feature selection. Patient data are encrypted using RSA-2048. The model is evaluated using the ‘Disease Prediction Using Machine Learning’ dataset, which contains demographic and clinical attributes for predicting diseases such as diabetes. Experimental results demonstrate strong predictive performance with 99.87% accuracy, 98.46% precision, 98% recall, and a 97.53% F1-score. In addition, the blockchain layer provides high operational reliability, achieving 99.99% data integrity and system availability, 100% auditability, and 99.8% data-sharing efficiency. The platform supports approximately 1,500 transactions per minute and implements role-based access control through smart contracts with an execution time of 0.2 s. Overall, the integrated AI–blockchain framework offers a scalable, transparent, and privacy-preserving solution suitable for real-world healthcare applications, including hospital decision-support systems and remote patient monitoring.
This study introduces an integrated conceptual framework for the synergy of artificial intelligence, blockchain, and big data analytics (BDA) as an enabler of sustainable competitive advantage in logistics systems. While the existing literature has extensively explored these technologies separately, the literature is still inconclusive on how these three technologies together support sustainable logistics. To address this gap, the study is conceptual and adopts a systematic and integrative literature review from 2008 to 2025. Guided by a PRISMA–inspired approach, the research identified 92 articles for review and performed a thematic synthesis. The research finds that digital sustainability is a result of the integration of technologies, rather than a mere effect of their individual contribution. Drawing from the lens of the resource-based view, dynamic capabilities theory, and triple bottom line framework, the research conceptualises BDA (as sensing), artificial intelligence (as seizing), and blockchain (as reconfiguring) as complementary elements that collectively contribute to a higher-level construct called digital sustainability capability, thereby creating a digital sustainability competitive advantage between a firm’s digital resources and its economic, environmental, and social performance. This research contributes to the literature by identifying a theoretical gap among fragmented streams of literature and by conceptualising a system-level view of digital transformation for digital sustainability in the supply chain. The research offers managerial implications for how firms can achieve digital sustainability by aligning their digital initiatives and sustainability objectives. Further, the research also suggests areas for future research, including empirical testing of the conceptualised framework, development of measures to assess the level of digital sustainability capability, and contextually specific explorations.
This article examines whether the procedural framework of the Federal Tax Ombudsman (“FTO”) in Pakistan, established under the Establishment of the Office of Federal Tax Ombudsman Ordinance, 2000, to adjudicate complaints of maladministration arising under federal fiscal statutes, may be strengthened through the integration of Kleros, a blockchain-based crowdsourced dispute resolution mechanism. Drawing upon an original empirical dataset of one hundred and twenty-four cases registered between January 2023 and February 2025, the article finds that the average resolution period of cases before the FTO is approximately 191.3 days, rising to 503.3 days for complex matters that traverse review, representation, and remand, whereas the Kleros mechanism resolves disputes in an average of 13.23 days across 2,111 adjudicated cases. The article also situates its findings within institutional economics, identifying the FTO as a hierarchical governance structure and the Kleros mechanism as a market-based alternative. It views the difference in resolution times as a measure of transaction costs for taxpayers and administration. By measuring these costs, the article depicts that a market-based adjudicatory system significantly reduces them, enhancing institutional efficiency. It provides empirical evidence, illustrating the welfare gains from institutional substitution in transaction cost economics and institutional change. Against this benchmark, three integration models are proposed, namely a hybrid concurrent fact-finding model, a delegated crowdsourcing model with conditional executive review, and an amicus curiae model for technically complex matters such as the taxation of digital assets, each anchored in the updates introduced under Kleros V2, including Soulbound Tokens that enable expert-gated juror selection. The article identifies two structural gaps that necessitate reform, namely the revolving-door capture within the FTO secretariat and the jurisprudential bottleneck created at the Presidential secretariat following the jurisprudence of the Supreme Court of Pakistan. It concludes that phased, pilot-based integration, commencing with the amicus curiae model in respect of complex subject-matter complaints, is jurisprudentially defensible, economically efficient, and operationally feasible within the legal framework of Pakistan.
Cloud-based academic environments such as Learning Management Systems (LMS), Open Journal Systems (OJS), institutional repositories, and web applications face increasing cybersecurity challenges due to heterogeneous users, distributed services, and extensive exposure to public networks. Existing security approaches remain fragmented, where machine learning focuses on threat detection, Zero Trust Architecture (ZTA) emphasizes access control, and blockchain is primarily used for secure logging. The lack of integration among these components limits the ability of security systems to adapt dynamically to evolving cyber threats. This study proposes an Adaptive Cybersecurity Framework (ACF) that integrates unsupervised machine learning-based anomaly detection, a risk-based Zero Trust Policy Engine, and blockchain-based immutable audit logging within a continuous adaptive feedback loop. The framework was evaluated using 450,000 anonymized HTTP and Web Application Firewall (WAF) events collected from a multi-domain academic cloud environment consisting of LMS, OJS, repositories, and supporting web applications. The analysis revealed structured and repetitive attack behaviors dominated by automated endpoint probing and cross-domain propagation patterns, indicating ecosystem-level security threats. The proposed risk assessment mechanism demonstrated effective alignment between anomaly detection and policy-based decision making. Experimental results achieved an AUROC of 0.7296 for risk-based threat detection while maintaining an average decision latency of approximately 11 ms, indicating suitability for real-time deployment. Blockchain integration further provided verifiable, tamper-resistant audit trails for mitigation actions and policy enforcement activities. This study contributes an ecosystem-aware adaptive cybersecurity paradigm that bridges threat detection, policy enforcement, and auditability through a unified security architecture for Academic Cloud Environments.
The exponential emergence of cross-chain data sharing in blockchain-enabled IoT and cloud systems creates vital challenges in the scalability, privacy, and post-quantum security landscapes. To tackle these problems, we propose a hierarchical attribute clustering-attribute-based encryption (HAC-ABE) scheme in this paper, which offers a secure post-quantum cross-blockchain data exchange framework. The method utilizes hierarchical attribute clustering and lattice-based encryption to reduce the computation overhead while supporting fine-grained access control. It utilizes IPFS decentralized storage and smart contracts to achieve a transparent data exchange across chains. Experimental results show 9.7% faster computation time and much lower communication overheads than state-of-the-art ABE-based approaches, verifying its effectiveness and scalability for practical decentralized environments.