Neide Judith Faria de Oliveira, Francisco Carneiro da Silva Filho
A pesquisa discute o uso da tecnologia NFT (Non-Fungible Tokens) no mercado de arte, considerando a exclusividade dessas obras. As NFTs permitem exibição em galerias virtuais no Metaverso, associadas ao blockchain, tanto como ativos físicos quanto intangíveis (propriedade intelectual). A tecnologia reforça noções de herança e propriedade, mas enfrenta desafios como altos custos, falta de regulamentação, consumo de energia e direitos autorais. A pesquisa explora as potencialidades dessa tecnologia e a necessidade de procedimentos claros para garantir transações justas e transparentes no mercado artístico.
Arshad Khan, Butch Dela Cruz, Narayan Nepal, Faheem Khan · 5 authors
This paper presentse-PrescripChain, a novel blockchain-based electronic prescription system enhanced with federated learning (FL) to address critical security, privacy, and efficiency challenges in traditional e-prescription platforms. While existing systems like New Zealand’s NZePS have improved prescription accuracy, centralized architectures remain vulnerable to fraud and data breaches, as evidenced by real-world incidents.e-PrescripChainleverages Ethereum smart contracts for tamper-proof prescription management, IPFS for decentralised storage of sensitive data, and FL for privacy-preserving collaborative fraud detection across healthcare institutions. Experimental results demonstrate the system’s practicality: AES-256 encryption handles 500 KB prescriptions in under 500 ms, while IPFS ensures reliable data retrieval (80–100 ms access times). By combining blockchain’s immutability with FL’s distributed intelligence, the framework achieves compliance with healthcare regulations (e.g., NZ Privacy Act 2020), mitigates single points of failure, and enables real-time prescription tracking. This work advances secure e-prescription systems by addressing the limitations of centralised models through a scalable, patient-centric approach.
BACKGROUND The convergence of AI, Blockchain (BC) technology, and healthcare represents one of the most transformative but technically challenging frontiers in computational medicine. As healthcare systems worldwide transition toward data-driven paradigms for precision medicine, clinical decision support, and population health management, the imperative for secure, privacy-preserving, and collaborative learning frameworks has reached critical importance. This tutorial presents the first comprehensive framework integrating Federated Learning (FL) and BC} for secure, privacy-preserving healthcare analytics. While FL offers collaborative training across distributed institutions without raw data sharing (aligning with HIPAA/GDPR), it faces vulnerabilities like model poisoning and gradient leakage. We introduce Blockchain-based Federated Learning (BCFL), leveraging BC's immutable ledger and decentralized consensus for enhanced trust, verifiability, and auditability. Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully, semi, loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance; (3) comprehensive security analysis of healthcare-specific vulnerabilities and mitigation via advanced cryptography like zero-knowledge proofs, homomorphic encryption and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI/Achine-Learning (ML) medical devices. We demonstrate BCFL's effectiveness across critical healthcare applications (e.g., disease prediction, medical imaging, patient monitoring, drug discovery) and identify emerging research frontiers including quantum-resilient cryptography, scalable interoperability, healthcare-specific incentives, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, collaborative AI in healthcare, accelerating privacy-preserving analytics, and ultimately improving patient outcomes. OBJECTIVE The objective of the paper is to present the first comprehensive tutorial on integrating Federated Learning (FL) and Blockchain (BC) technologies specifically for secure, privacy-preserving healthcare analytics. The motivation stems from the growing need for collaborative healthcare data analysis that adheres to stringent privacy regulations like HIPAA and GDPR, especially as traditional centralized models pose significant data security risks. The authors aim to address the vulnerabilities of FL, such as model poisoning and gradient leakage, by leveraging BC’s features like decentralization, immutability, and auditability. The tutorial is designed to guide researchers, practitioners, and policymakers in understanding and implementing secure AI systems in the medical domain. METHODS To achieve this goal, the authors develop a multi-faceted framework by first creating a comprehensive taxonomy of medical data types and their specific requirements for FL deployment. They then propose three novel integration architectures—fully coupled, semi-coupled, and loosely coupled—each analyzed for its security, scalability, and compliance with healthcare regulations. The tutorial includes an in-depth security analysis addressing threats unique to healthcare, and explores privacy-enhancing technologies such as zero-knowledge proofs, homomorphic encryption, and differential privacy. It also introduces a regulatory compliance framework aligned with HIPAA, GDPR, and FDA guidelines for AI/ML-based medical devices. Throughout, the methodology integrates technical depth with practical implementation advice. RESULTS The results of this study are delivered through a set of clearly articulated contributions. The proposed architectures and frameworks are demonstrated to significantly enhance trust, verifiability, and auditability in healthcare FL systems, making them more robust against known threats. The paper effectively showcases how BCFL (Blockchain-based Federated Learning) can be applied to real-world healthcare use cases such as disease prediction, patient monitoring, medical imaging, and drug discovery. Additionally, it outlines emerging research directions, including quantum-resilient cryptography, scalable interoperability, incentive mechanisms for healthcare data sharing, and automated compliance monitoring. These outcomes position the tutorial as a foundational reference for advancing secure and compliant collaborative AI in healthcare. CONCLUSIONS This tutorial presented the first comprehensive framework integrating FL and BC for secure, privacy-preserving healthcare analytics. We demonstrated how FL enables decentralized model training across healthcare institutions while maintaining data locality, and how BC enhances trust, integrity, and auditability through immutable ledgers and decentralized consensus mechanisms. Our key contributions include: (1) a systematic taxonomy of diverse medical data types and their FL requirements; (2) three novel integration architectures (fully coupled, semi-coupled, and loosely coupled) with rigorous analysis of security, scalability, and regulatory compliance trade-offs; (3) comprehensive security analysis identifying healthcare-specific vulnerabilities and mitigation strategies using advanced cryptographic techniques including zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a practical regulatory compliance framework addressing HIPAA, GDPR, and FDA guidelines for AI}/ML-based medical devices. We validated BCFL effectiveness across critical healthcare applications including disease prediction, medical imaging analysis, patient monitoring, and drug discovery. Looking ahead, crucial research frontiers involve quantum-resilient cryptography, scalable interoperable infrastructure, healthcare-specific consensus mechanisms, and automated compliance frameworks. This tutorial serves as a foundational reference for developing trustworthy, interoperable, and patient-centric AI systems that transform healthcare delivery while ensuring privacy protection and regulatory compliance. The successful realization of secure collaborative healthcare analytics through BCFL will drive improved patient outcomes and accelerate medical discoveries in an increasingly connected healthcare ecosystem. CLINICALTRIAL N/A
ABSTRACT Blockchain, an immutable, decentralized, and distributed ledger technology, has the potential to revolutionize the sharing and access of confidential data, particularly in the healthcare sector, where patient‐centric systems benefit greatly from its robust security features and ability to ensure data integrity. Recognizing the potential, this paper reviews current research on blockchain‐based healthcare architectures that focus on patient‐centric solutions, exploring the present state of these architectures and highlighting the growing interest in blockchain applications for enhancing healthcare. The review identifies key trends, challenges, and opportunities within this domain, analyzing the evolution of research over recent years, the types of studies conducted, their geographical distribution, and publication channels. It delves into the prevalent use cases, primary challenges addressed, and contributions of various studies, covering common blockchain platforms, types of blockchains, and the implementation of smart contracts. Guided by four critical questions aimed at improving security and privacy, streamlining patient consent management, fostering interoperability, and ensuring compliance with healthcare data regulations, this research underscores the importance of patient data security through advanced encryption techniques, access controls, and decentralized storage solutions. It also highlights the benefits of patient‐controlled data sharing, empowering individuals with greater control over their health information. Despite progress, the review identifies areas requiring further exploration, such as advanced security threats, nuanced and dynamic consent models, comprehensive interoperability solutions, and alignment with evolving healthcare regulations. Addressing these gaps necessitates research into scalable privacy‐preserving blockchain architectures that prioritize dynamic consent management and interoperability standards, thus making regulatory compliance possible in an ever‐changing healthcare environment.
Saad Alahmari, Amal Alshardan, Fahd N. Al‐Wesabi, Shaymaa E. Sorour · 8 authors
As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted.
Angela Hemesath, William M. Tian, Bryce W. Polascik, Suzanna Joseph · 10 authors
Purpose:. To combine the perspectives of health and commercialization experts on the ethical and regulatory needs for non-fungible token (NFT) implementation in healthcare.Design:. PerspectiveMethods:. For a multidisciplinary perspective by an interdisciplinary group, current event articles and research articles were interpreted and assessed.Results:. Health data has become fragmented and disorganized, resulting in poor accessibility, increased administrative costs, and integrity vulnerability. Healthcare is uniquely suited to adopt blockchain and NFT technology as potential solutions. The incorporation of blockchain technology may offer multiple improvements in data-sharing through consensus, tokenization, and decentralization. However, the current regulatory infrastructure to support blockchain is poorly defined.Conclusions:. Healthcare NFTs would revolutionize patient control over their health data and promote more ethical transparency of data ownership while also reducing administrative security costs. However, blockchain poses unprecedented requirements of healthcare regulation within the unique realms of patient privacy and data ownership. Large-scale implementation of blockchain cannot be achieved without regulatory collaboration.
Open access
Pharmaceutical Economics and Policy
Health Systems, Economic Evaluations, Quality of Life
This paper presents a scalable, secure blockchain-based healthcare system architecture that efficiently manages large patient datasets. DHTs and Skip Lists enable efficient data access, while DPoS and PBFT facilitate parallel transaction processing. Adaptive filters, Radix Trees extended by Merkle Trees, and an immutable blockchain ledger secured by Tendermint consensus ensure data integrity and protection against evolving threats. Threshold Cryptography secures consensus participant selection, and Bulletproofs verify transactions, complying with healthcare regulations. ChaCha20, a symmetric stream cipher, encrypts sensitive data, enhancing performance across devices. ABAC manages access rights, ensuring fine-grained control over data accessibility. This architecture offers a comprehensive, efficient, and secure solution for healthcare data management in blockchain environments.
This chapter delves into the innovative application of blockchain-based smart contracts in the realm of healthcare cybersecurity, presenting a comprehensive analysis of their potential to automate compliance and regulatory adherence. A key focus of the chapter is the examination of how smart contracts can be programmed to enforce compliance with stringent healthcare regulations such as HIPAA and GDPR. This involves a critical analysis of the legal, security, and technical challenges associated with their deployment. The chapter also investigates the potential of smart contracts to streamline compliance processes, reduce administrative burdens, and minimize the risks associated with human error, thereby enhancing the efficiency and reliability of healthcare services. The comparative analysis of various blockchain platforms supporting smart contracts forms a significant part of the discussion. This analysis evaluates the platforms across multiple criteria relevant to healthcare cybersecurity, including scalability, interoperability, and financial metrics. The chapter employs the Analytic Hierarchy Process (AHP) to structure this comparison, providing a quantitative evaluation of each platform's suitability for healthcare applications. Innovative applications of smart contracts in healthcare cybersecurity are highlighted, showcasing their versatility and potential for widespread adoption. The chapter concludes with a synthesis of findings and a discussion on future directions in the application of smart contracts in healthcare cybersecurity. It reflects on the broader implications of the study and suggests areas for further research, emphasizing the need for continued development and collaboration among healthcare professionals, technologists, and legal experts.
Adenilda Maria Siqueira de Andrade, Alberice Maria Mendes, Cristiane S. Miguel Cabral de Vasconcelos, Maria Eliza da Mota Reinaux Paes Barreto
O Brasil vem redefinindo o perfil do seu sistema de saúde, reformulando papéis e funções de saúde pública na tentativa de construir responsabilidades locais. Este panorama originou um novo arranjo para o sistema municipal de saúde no qual busca-se a integralidade das ações de saúde a partir da dinâmica do financiamento. Trata-se de um estudo descritivo de base bibliográfica e documental. O estudo apresenta uma revisão sobre o processo da descentralização administrativa/financeira na saúde, sua repercussão no fortalecimento da atenção básica tendo a estratégia saúde da família como elemento propulsor da forte expansão observada nesse componente da atenção à saúde, bem como as conseqüências da descentralização na gestão dos trabalhadores de saúde. Discute o papel das Normas Operacionais do SUS, em especial da Norma Operacional Básica - NOB/96 com a implantação do Piso da Atenção Básica (PAB) como referencial para a ampliação dos investimentos na atenção básica, o papel relevante da Lei de Responsabilidade Fiscal que além de impor limites aos gastos, também estabelece diretrizes para a elaboração, execução e avaliação do orçamento público, e o Pacto pela Saúde firmado entre as três esferas de governo com a atenção voltada para os princípios e diretrizes articulados e integrados nos Pactos pela Vida, em Defesa do SUS e de Gestão. Destaca-se o forte papel indutor do nível federal de gestão do SUS, que através de mecanismos de financiamento reordena as ações e serviços de saúde no nível local