This study proposes a hybrid blockchain system for secure and transparent data management in multinational space missions. By combining public and private blockchains, the model enables open access to non-sensitive data while protecting confidential mission records. Data integrity is ensured through cryptographic proofs without exposing the underlying content, and a cross-chain protocol enables real-time synchronization between chains without relying on centralized intermediaries. The system was implemented using Ethereum and Hyperledger Fabric and tested with real extravehicular activity data. Results show that it effectively detects data tampering, enforces access control, and synchronizes records with low latency. Compared to traditional centralized systems, this approach offers improved resilience, auditability, and trust across organizations. It provides a practical foundation for future space data infrastructures requiring both transparency and confidentiality.
The article examines topical aspects of financial support for sustainable development of local communities in decentralization, martial law, and institutional transformation of the public administration system. The concept of internal and external sources of financing that constitute the resource base for local economic development is revealed, and the need for their balanced use to ensure social stability, infrastructure modernization, and economic autonomy of territorial communities is justified. The primary forms of internal resources are structured revenues to local budgets, income from communal property, own services, and the potential of external sources — interbudgetary transfers, grant programs, investments, loan instruments, and public-private partnerships — outlined. Emphasis is placed on the importance of developing human, social, and institutional capital as key intangible resources of local self-government bodies that ensure the effective implementation of strategic initiatives. Particular attention is paid to modern mechanisms for attracting financing, including municipal bonds, crowdfunding, voucher mechanisms, preferential lending, and corporate social responsibility. It was emphasized that increasing the financial capacity of local communities requires local self-government bodies to have high managerial competence, openness to partnerships, strategic thinking, and the ability to mobilize both internal and external resources. The article substantiates the feasibility of applying a comprehensive approach to forming a resource base for local development, combining financial, organizational, managerial, and communication aspects. The study results are of theoretical importance for deepening the scientific foundations of regional development and practical value for the formation of strategies to increase the financial self-sufficiency and investment attractiveness of communities in conditions of crisis transformations. Keywords: territorial communities; local economic development; financial security; internal resources; external sources of financing; budget autonomy; investments; grants; municipal finances; credit mechanisms; social capital; management capacity; decentralization; public administration; sustainable development.
Smart contracts are self-executing programs stored on blockchain, ensuring secure and transparent execution of digital agreements.This minicourse aims to present the fundamental concepts, advantages, applications, and challenges of smart contracts. ResumoOs smart contracts (contratos inteligentes) so programas autoexecutveis armazenados em blockchain, garantindo execuo segura e transparente de acordos digitais.Este minicurso tem como objetivo apresentar os conceitos fundamentais, vantagens, aplicaes e desafios dos smart contracts.
The article explores the transformation of public administration mechanisms in the healthcare system under conditions of decentralization. The study aims to analyze the impact of decentralization on the transformation of public administration in the healthcare sector of Ukraine and to identify the challenges and directions for improving its management processes. The research examines current healthcare reforms being implemented in Ukraine, particularly the introduction of the National Health Service, the electronic healthcare system, new financing mechanisms through the Medical Guarantee Program, and the decentralization of powers to the local level. The dynamics of healthcare funding in Ukraine for 2023–2024 are analyzed, indicating the continued prioritization of the sector amidst public administration reforms. A positive trend has been observed in the increased budget allocations for specialized care, centralized procurement of medicines, emergency response, and other key areas. The analysis of international experience shows that the effectiveness of decentralization depends on the fiscal autonomy of communities, managerial capacity, transparency in decision-making, and precise coordination between levels of government. The study substantiates that decentralization opens new opportunities to improve the efficiency and accessibility of healthcare services but is accompanied by several challenges: staff shortages, lack of unified medical service standards, and inequality in access to healthcare. To address these challenges, the paper justifies directions for transforming public administration mechanisms through enhancing professional capacity, standardization, digital transformation of management processes, and developing a culture of accountability. The proposed directions for transformation will help ensure equal access to medical services, strengthen the managerial capacity of local self-government bodies, reduce administrative risks, increase transparency and public trust, and promote the innovative development of the healthcare sector through digitalization. Keywords: public administration mechanisms, decentralization, healthcare, public administration, budgetary fundings.
Money laundering has long been a major issue for governments, law enforcement agencies, and financial institutions around the globe. As technology advances, so too do money laundering methods, presenting new challenges for authorities and financial entities. Organised Crime Groups (OCGs) are increasingly exploiting digital platforms, cryptocurrencies, and virtual assets to disguise illicit funds while maintaining anonymity and complicating their transactions. This article analyses the problem of cryptocurrency laundering by the OCGs and various tactics employed by the OCGs to cover their trails. This article also in-depth discusses the international instruments such as the United Nations Convention against Transnational Organised Crime and Financial Action Task Force recommendations on the prevention of cryptocurrency laundering. The special focus of this paper is on the legal framework regarding cryptocurrency laundering in the United States, European Union and Malaysia. The findings of the paper suggest that there is a regulatory framework present in these jurisdictions but their regulations are not subject specific and regulatory powers have been granted to the authorities that are not specialised and skilled to tackle the problem of combating cryptocurrency laundering by OCGs.
General background: The increasing integration of digital technologies has transformed global financial systems, with cryptocurrencies, especially Bitcoin, emerging as prominent financial instruments. Specific background: Amid widespread adoption by institutions and individuals, Bitcoin has garnered attention for its potential to influence traditional financial markets, particularly during periods of global uncertainty such as the COVID-19 pandemic. Knowledge gap: While much has been discussed about the theoretical influence of cryptocurrencies, empirical evidence on their actual impact on global financial indices remains inconclusive. Aims: This study investigates the effect of Bitcoin trading volume and the COVID-19 pandemic on a composite index comprising advanced (S&P 500), emerging (KLSE), and developing (DZ) market indices from July 2018 to December 2022. Results: Using a fixed-effects panel data model, the findings reveal that past market performance significantly predicts current performance, while Bitcoin trading volume and the pandemic show no statistically significant impact. Novelty: The study uniquely combines market classifications and utilizes a composite index to empirically isolate the influence of Bitcoin across diverse economies. Implications: These results suggest that, despite Bitcoin's rising prominence, its direct influence on global financial markets may be limited in the short term, underscoring the need for continued investigation as regulatory frameworks and adoption rates evoHighlight : Minimal Impact: Bitcoin trading volume and the COVID-19 pandemic had no statistically significant effect on global financial market indices (2018–2022). Strong Market Correlation: Global financial indices showed strong interdependence, reflecting synchronized market behavior. Future Outlook: Despite current findings, evolving crypto regulations and technologies may alter their financial market influence. Keywords : Cryptocurrencies, Bitcoin, Trading Volume, COVID-19, Financial Indices
ABSTRACT This research examined the connection between Bitcoin, the prominent and extensively mined cryptocurrency, and CO 2 emissions using the SVAR model. Azerbaijan, Kazakhstan, and Russia, the three main countries in the Caspian Basin that are the centre of cryptocurrency mining, were examined in terms of their primary industries. The variance decomposition analysis indicated that the Bitcoin price had the most significant explanatory role in CO 2 emissions released by Oil and Natural Gas industry in Azerbaijan. When it comes to the CO 2 emissions that were emitted by the Petroleum Refining‐Manufacture of Solid Fuels and Other Energy industry, as well as Manufacturing Industries and Construction, the Bitcoin price had the most important effect in Kazakhstan. There was a significant contribution made by Bitcoin to the CO 2 emissions that were emitted by the Manufacturing Industries and Construction in Russia. The impulse response functions illustrated a strong association between Bitcoin and CO 2 emissions. However, in contrast to existing research, this relationship was found to be negative. The increase in energy usage during Bitcoin price falls can be attributed to the need to compensate for losses, particularly in the mining process. To diminish this connection, the dependence of the cryptocurrency on fossil fuels must be minimised.
This paper addresses the challenge of creating smart contracts for applications represented using Business Process Management and Notation (BPMN) models. In our prior work we presented a methodology that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN flow control, making it independent of the underlying blockchain infrastructure, with only the BPMN task elements requiring coding. In subsequent research, we enhanced our approach by adding support for nested transactions and enabling a smart contract repair and/or upgrade. To empower Business Analysts (BAs) to generate smart contracts without relying on software developers, we tackled the challenge of generating smart contracts from BPMN models without assistance of a software developer. We exploit the Decision Model and Notation (DMN) standard to represent the decisions and the business logic of the BPMN task elements and amended our methodology for transformation of BPMN models into smart contracts to support also the generation script to represent the business logic represented by the DMN models. To support such transformation, we describe how the BA documents, using the BPMN elements, the flow of information along with the flow of execution. Thus, if the BA is successful in representing the blockchain application requirements using BPMN and DMN models, our methodology and the tool, called TABS, that we developed as a proof of concept, is used to generate the smart contracts directly from those models without developer assistance.
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars throughout the blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code reviews and formal verification face limitations in scalability, automation, and adaptability to evolving development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models. This paper analyzes how each technique represents code, processes semantic information, and responds to real world vulnerability classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability, computational overhead, and real time applicability. Lastly, it highlights open challenges and future opportunities for advancing this domain.
We construct an empirically founded model of a repo trade intermediated by two broker-dealers and prove multiple equilibrium and the existence of equilibrium at the joint profit maximizing volume of trade. We then present a smart contract that resolves multiple equilibrium by requiring each broker-dealer to report its client schedule and its minimum hurdle spread, and implementing a selection rule that filters out hurdle-infeasible outcomes. Whenever there exists an equilibrium that exceeds both hurdle spreads, the protocol selects the joint profit maximizing feasible trade and thereby avoids a collapse to no trade. The smart contract is a machine executed algorithm which eliminates the need for trust. Hardware and cryptography are used to prevent leakage of broker-dealer client trade schedules, and to enable privacy-protected auditing with zero-knowledge proofs of the integrity of computations. The outcome can be implemented by a myopic strategy where a broker-dealer truthfully reports its own variables without anticipating its counterparty's reports. This minimizes cognitive and computational complexity, thereby making our smart contract suitable for real-world deployment.
We present the first formal treatment of \emph{yield tokenization}, a mechanism that decomposes yield-bearing assets into principal and yield components to facilitate risk transfer and price discovery in decentralized finance (DeFi). We propose a model that characterizes yield token dynamics using stochastic differential equations. We derive a no-arbitrage pricing framework for yield tokens, enabling their use in hedging future yield volatility and managing interest rate risk in decentralized lending pools. Taking DeFi lending as our focus, we show how both borrowers and lenders can use yield tokens to achieve optimal hedging outcomes and mitigate exposure to adversarial interest rate manipulation. Furthermore, we design automated market makers (AMMs) that incorporate a menu of bonding curves to aggregate liquidity from participants with heterogeneous risk preferences. This leads to an efficient and incentive-compatible mechanism for trading yield tokens and yield futures. Building on these foundations, we propose a modular \textit{fixed-rate} lending protocol that synthesizes on-chain yield token markets and lending pools, enabling robust interest rate discovery and enhancing capital efficiency. Our work provides the theoretical underpinnings for risk management and fixed-income infrastructure in DeFi, offering practical mechanisms for stable and sustainable yield markets.
This article examines the impact of non-fungible tokens (NFTs) on contemporary digital culture. Purpose. It aims to understand the role of NFTs from the perspective of the hybridization between art and social sciences, evaluating their applications and intersection with different cultural formats. Methodology. A critical and exhaustive literature review was conducted, applying inclusion and exclusion criteria through the PRISMA protocol to ensure the validity of the analyzed studies. Results and conclusions. Findings suggest that NFTs have high potential to transform digital culture by enabling new forms of ownership, authenticity, and interaction across industries such as art, music, and gaming. However, they also pose challenges regarding regulation, sustainability, and financial speculation. Original contribution. This study highlights the relevance of NFTs in today’s creative industries, assessing their feasibility as a cultural management tool and their role in redefining the concept of digital ownership within a decentralized environment.
Caixiang Fan, Amirhossein Sohrabbeig, Petr Musı́lek
Blockchain-based peer-to-peer energy trading enables individuals to directly share renewable energy using Internet of Things technologies. However, it faces significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence. To address these issues, this article proposes zkPET, a secure and intelligent peer-to-peer energy trading framework. zkPET integrates machine learning and blockchain with advanced cryptographic techniques of zero-knowledge machine learning to protect user data while enabling intelligent decision making. In the zkPET framework, the computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording. In addition, a time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation. Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET. The results underscore its potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading, contributing to the advancement of secure and intelligent energy markets.
Peter KimemiahMwangi, Stephen TNjenga, Gabriel Ndung’uKamau
Directed Acyclic Graph (DAG) based Distributed Ledger Technologies (DLTs) are being explored to address the scalability and energy efficiency challenges of traditional blockchain in IoT applications. The objective of this research was to gain insight into algorithms predicting how IoT-DAG DLT horizontal scalability changes with increasing node count in a heterogeneous ecosystem of full and light nodes. It specifically questioned how incorporating preferential attachment topology impacts IoT network scalability and performance, focusing on transaction throughput and energy efficiency. Using an AgentBased Modelling (ABM) simulation, the study evaluated a heterogeneous 1:10 full/light node network with Barabási Albert Preferential Attachment (PA-2.3) across increasing node counts (100-6400). Performance was measured by Confirmed Transactions Per Second (CTPS) and Mean Transaction Latency (MTL). Results showed CTPS scales linearly with node count (R² ≈ 1.000), exhibiting robust predictability. MTL increased logarithmically (R² ≈ 0.970), becoming more predictable as the network grew. Horizontal scalability showed exponential decay. The study confirms that IoT-DAG DLTs with preferential attachment can achieve predictable, near-linear throughput horizontal scalability, highlighting that topology matters and optimising CTPS yields the highest throughput gains.
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or violate policy, producing risks driven by emergent multi-agent dynamics rather than classical software bugs. This position paper maps the security agenda for cross-domain multi-agent LLM systems. We introduce seven categories of novel security challenges, for each of which we also present plausible attacks, security evaluation metrics, and future research guidelines.
Pierre Sedi Nzakuna, Vincenzo Paciello, A. Lay-Ekuakille, Angelo Kuti Lusala · 6 authors
The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol-a leaderless, feeless DAG designed for the IoT-with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT.
Studi ini bertujuan untuk memetakan perkembangan dan tren riset global mengenai Non-Fungible Token (NFT) melalui pendekatan bibliometrik menggunakan data dari basis Scopus dan visualisasi VOSviewer. Hasil analisis menunjukkan bahwa riset NFT mengalami peningkatan signifikan sejak 2020, dengan dominasi tema seperti smart contract, digital assets, dan blockchain. India, Tiongkok, dan Inggris tercatat sebagai kontributor utama literatur NFT global. Analisis kata kunci mengungkap transisi tematik dari fokus seni digital menuju bidang teknologi informasi, keamanan siber, dan data kesehatan. Visualisasi temporal dan densitas mengindikasikan bahwa topik-topik baru seperti cybersecurity, interplanetary file system, dan electronic health record menjadi pusat perhatian terkini dalam literatur NFT. Studi ini memberikan kontribusi penting dalam memahami struktur, aktor, dan arah perkembangan penelitian NFT, serta menawarkan rekomendasi strategis bagi peneliti dan pengambil kebijakan untuk mengembangkan riset NFT yang inklusif dan berkelanjutan.
Background In recent years, the rise of “AI+arts” has increased public attention towards emerging digital collectibles and garnered significant interest among young adult collectors globally. However, there has been limited investigation into how emerging media effects may influence consumers’ purchase of digital collectibles from the perspective of relevant theories, particularly in collectivistic cultural contexts. To address this gap, the present study is guided by the extended Theory of Planned Behavior (TPB), integrated with ideal self-congruence, and rigorously examines the effect of exposure to Non-Fungible Token digital art information on the intention to purchase digital collectibles among young Chinese adults (aged 18–34). Methods A total of 259 responses were obtained through an online survey. Statistical analyses, including direct, indirect, and serial mediation, were conducted using SPSS 25.0 and Jamovi 2.6.24. Results The findings indicate that both TPB and ideal self-congruence act as mediators in this relationship. Additionally, a serial mediation process involving ideal self-congruence and attitudes toward intelligence was identified. Conclusion These findings provide valuable insights into the complex factors influencing the purchase intention of digital collectibles among young Chinese adults. Furthermore, the findings offer recommendations for digital collectible platforms and relevant stakeholders.
Open access
Digital Marketing and Social Media
Art History and Market Analysis
Consumer Behavior in Brand Consumption and Identification
Proof-of-Work (PoW) consensus is traditionally analyzed under the assumption that all miners incur similar costs per unit of computational effort. In reality, costs vary due to factors such as regional electricity cost differences and access to specialized hardware. These variations in mining costs become even more pronounced in the emerging paradigm of \emph{Proof-of-Useful-Work} (PoUW), where miners can earn additional \emph{external} rewards by performing beneficial computations, such as Artificial Intelligence (AI) training and inference workloads. Continuing the work of Fiat et al., who investigate equilibrium dynamics of PoW consensus under heterogeneous cost structures due to varying energy costs, we expand their model to also consider external rewards. We develop a theoretical framework to model miner behavior in such conditions and analyze the resulting equilibrium. Our findings suggest that in some cases, miners with access to external incentives will optimize profitability by concentrating their useful tasks in a single block. We also explore the implications of external rewards for decentralization, modeling it as the Shannon entropy of computational effort distribution among participants. Empirical evidence supports many of our assumptions, indicating that AI training and inference workloads, when reused for consensus, can retain security comparable to Bitcoin while dramatically reducing computational costs and environmental waste.
Joel Lidin, Amir Sarfi, Evangelos Pappas, Samuel Dare · 6 authors
We describe an incentive system for distributed deep learning of foundational models where peers are rewarded for contributions. The incentive system, \textit{Gauntlet}, has been deployed on the bittensor blockchain and used to train a 1.2B LLM with completely permissionless contributions of pseudo-gradients: no control over the users that can register or their hardware. \textit{Gauntlet} can be applied to any synchronous distributed training scheme that relies on aggregating updates or pseudo-gradients. We rely on a two-stage mechanism for fast filtering of peer uptime, reliability, and synchronization, combined with the core component that estimates the loss before and after individual pseudo-gradient contributions. We utilized an OpenSkill rating system to track competitiveness of pseudo-gradient scores across time. Finally, we introduce a novel mechanism to ensure peers on the network perform unique computations. Our live 1.2B run, which has paid out real-valued tokens to participants based on the value of their contributions, yielded a competitive (on a per-iteration basis) 1.2B model that demonstrates the utility of our incentive system.
CLASS is a proof-of-concept general purpose linear programming language, flexibly supporting realistic concurrent programming idioms, and featuring an expressive linear type system ensuring that programs (1) never misuse or leak stateful resources or memory, (2) never deadlock, and (3) always terminate. The design of CLASS and the strong static guarantees of its type system originates in its Linear Logic and proposition-as-types foundations. However, instead of focusing on its theoretical foundations, this paper briefly illustrates, in a tutorial form, an identifiable CLASS session-based programming style where strong correctness properties are automatically ensured by type-checking. Our more challenging examples include concurrent thread and memory-safe mutable ADTs, lazy stream programming, and manipulation of linear digital assets as used in smart contracts.
This paper introduces EarthOL, a novel consensus protocol that attempts to replace computational waste in blockchain systems with verifiable human contributions within bounded domains. While recognizing the fundamental impossibility of universal value assessment, we propose a domain-restricted approach that acknowledges cultural diversity and subjective preferences while maintaining cryptographic security. Our enhanced Proof-of-Human-Contribution (PoHC) protocol uses a multi-layered verification system with domain-specific evaluation criteria, time-dependent validation mechanisms, and comprehensive security frameworks. We present theoretical analysis demonstrating meaningful progress toward incentive-compatible human contribution verification in high-consensus domains, achieving Byzantine fault tolerance in controlled scenarios while addressing significant scalability and cultural bias challenges. Through game-theoretic analysis, probabilistic modeling, and enhanced security protocols, we identify specific conditions under which the protocol remains stable and examine failure modes with comprehensive mitigation strategies. This work contributes to understanding the boundaries of decentralized value assessment and provides a framework for future research in human-centered consensus mechanisms for specific application domains, with particular emphasis on validator and security specialist incentive systems.
Diabetic Retinopathy (DR) detection in distributed telemedicine environments requires secure, scalable, and privacy-preserving solutions. Traditional federated learning (FL) relies on a central server, raising concerns about data privacy and system trust. We propose a novel serverless framework, FL-BC-SMPC-SMOTE, that integrates deep learning, FL, secure multi-party computation (SMPC), the Synthetic Minority Over-sampling Technique (SMOTE), Blockchain (Hyperledger Fabric), and the InterPlanetary File System (IPFS) to address these challenges. Using the APTOS 2019 dataset, we trained CNN-based models (e.g., EfficientNet-B0, ResNet-18) across 2–10 clients, achieving approximately 90% accuracy without raw data sharing. SMPC eliminates the need for a central aggregator by distributing encrypted model updates among clients, enabling privacy-preserving learning. Blockchain ensures auditable and tamper-resistant aggregation, while IPFS significantly reduces communication overhead—from 64 GB to 100 KB per round. Local SMOTE enhances recall for minority classes by 10–15%, promoting equity in DR severity classification. Compared to differentially private baselines (52.18% accuracy), our framework delivers a robust balance of performance, privacy, and fairness. This GDPR/HIPAA-compliant solution offers a practical and trustworthy approach to decentralized DR detection in real-world telemedicine settings.
Zhen Chu, Wangjie Qiu, T. T. Lei, Jinchun He · 5 authors
The widespread adoption of emerging technologies in healthcare has led to an exponential increase in medical data generation. However, the security of healthcare data has not kept pace, with frequent breaches and unauthorized access posing substantial threats to patient privacy and the integrity of healthcare systems. Although existing access control frameworks offer partial solutions for secure data access, they fall short in authorization granularity, privacy preservation, and large-scale, high-frequency access. To bridge these critical gaps, we propose a novel role-based access control (RBAC) framework that enables secure and efficient management of large-scale, high-frequency data access. The framework first introduces a real-time access behavior analysis algorithm. It then integrates Ethereum smart contract technology with the RBAC model to construct high-performance, scalable access control contracts. Subsequently, the framework simulates the EMR interaction process in a representative healthcare scenario. Through rigorous security evaluations and experimental simulations, we demonstrate that the proposed framework enables robust accessor management, secure data sharing, and effective support for large-scale, high-frequency access while maintaining operational efficiency. This work offers a scalable and practical solution to healthcare data security in the era of big data and population aging.