BLOCKCHAIN-ENABLED SECURE MEDICAL DATA COMPRESSION AND SHARING FRAMEWORK

The digital transformation of healthcare is revolutionizing the management of medical institutions, improving operational efficiency, patient outcomes, and data security. With the increasing complexity of healthcare systems, the integration of cutting-edge technologies such as Artificial Intelligence. The COVID-19 pandemic significantly accelerated digital transformation, compelling healthcare institutions to adopt telemedicine, AI-assisted diagnostics, and cloud-based medical records to meet growing patient demands and resource constraints. The rapid digital transformation of healthcare is driven by advancements in Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data. This review article aims to analyze the objectives and implications of digital transformation in medical institutions, focusing on the integration of AI, blockchain, and IoT in hospital management. The methodological approach for this review article focuses on synthesizing existing literature to examine the role of Artificial Intelligence (AI), blockchain, the Internet of Things (IoT), and Big Data in the digital transformation of healthcare management. The integration of Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), and Big Data has demonstrated significant improvements in healthcare management, enhancing efficiency, patient outcomes, and data security.

BackgroundThe field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.MethodsWe retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.ResultsA total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.ConclusionThis study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.

The Internet of Things (IoT)services and application were increasing during the last years in several life fields causes need to provide a secure identifier for protecting sensing data passing between IoT sensors/devices and embedded-subsystem connected by networks. This paper was proposed an algorithm for helping in IoT communication security can used in different IOT entities used in unofficial industrial machine to machine (M2M) communications, smart energy-grids, home or buildings and other computing devices. 
 This paper proposed a secure system using new proposed 4D chaotic system combined with the modified lightweight Advanced Encryption Standard (AES). The proposed 4-dimension (4D) chaos system Lyapunov was tested and pass for many initial periods and get a super chaos system (4 positive Lyapunov). Generated chaos keys (used JORN) were used in the lightweight AES and the Secure Hash Algorithm version 3 (SHA3-256). The Lightweight AES was design in case to reduce CPU computation cycles and complexity of AES. Results show that computation time for proposed system decreased (has 145% speedup more). The output of modified lightweight AES encryption System has the good statistical tests near to original AES that can avoid many attacks.

The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders. In fact the last few years have registered an unprecedented rise in healthcare data breaches. Hacking incidents and privilege abuse are the most common threats and have exposed sensitive and protected health data. Experts and researchers are working on various techniques, tools, and methods to address the security issues related to healthcare data. In this article, the main focus is on evaluating the impact of research studies done in the context of healthcare data breach reports to identify the contemporary privacy and confidentiality issues of sensitive healthcare data. Analysis of the research studies depicts that there is a need for proactive security mechanisms that will help the healthcare organizations to identify abnormal user behavior while accessing healthcare data. Moreover, studies also suggest that ML techniques would be highly effective in securing the privacy and confidentiality of the healthcare data. Working further on this premise, the present study also proposes a conceptual framework that will secure the privacy and confidentiality of healthcare data proactively. The proposed framework is based on ML techniques to detect deviated user access against Electronic Health Records. Further, fuzzy-based Analytical Network Process (ANP), a multi-criteria decision-making approach, is used to assess the accuracy of the supervised and unsupervised ML approaches for achieving a dynamic digital healthcare data security environment.

The implementation of Electronic Medical Records (EMR) represents a major advancement in health information systems, playing a vital role in improving service efficiency, data security, and the quality of medical documentation. This study utilizes a literature review approach to assess EMR implementation in hospitals by analyzing findings from 20 relevant journals. Literature sources were obtained from indexed national scientific publications, with a focus on the benefits, challenges, and critical success factors of EMR adoption. The results reveal that EMR use can enhance service efficiency by up to 40%, expedite medical decision-making, and increase the accuracy of patient data. Additionally, EMR reduces the risk of data loss, facilitates coordination among healthcare professionals, and supports more effective integration of medical information. Despite these benefits, EMR implementation encounters several challenges, such as inadequate technological infrastructure, resistance from some healthcare personnel toward digital systems, and the need for ongoing training to ensure optimal use. If not addressed properly, these barriers may limit the system’s effectiveness. Key factors influencing successful implementation include strong management commitment, early involvement of end-users during the planning phase, and system flexibility to meet specific clinical requirements. Therefore, EMR implementation should adopt a holistic approach that integrates human resource readiness, technological capacity, and clear regulatory frameworks. With well-structured strategies, consistent institutional support, and comprehensive training programs, EMR has the potential to be a driving force in the digital transformation of healthcare. This transformation can lead to sustainable improvements in service quality, patient data security, and operational efficiency in hospital settings.

With the widespread adoption of Electronic Health Records (EHRs), ensuring data privacy while enabling efficient sharing and processing has emerged as a critical challenge in healthcare information systems. Existing approaches often struggle to balance stringent security requirements with practical performance needs. To address these challenges, this paper proposes a blockchain-based privacy protection scheme for EHRs that integrates Designated Verifier Aggregate Signatures (DVAS) and Fully Homomorphic Encryption (FHE). In the proposed scheme, healthcare institutions manage EHRs through smart contracts, while FHE is employed to encrypt and protect sensitive medical data used in research. By selectively applying FHE to critical data and minimizing encryption overhead for non-sensitive information, our design significantly enhances computational efficiency and storage performance. Furthermore, the DVAS algorithm minimizes redundant data transmission, ensures data integrity, and restricts signature verification to authorized parties only. Experimental evaluations based on computational time, storage cost, and security analysis demonstrate that the proposed scheme achieves robust security while meeting performance requirements, effectively balancing confidentiality and efficiency in healthcare information systems.

Chapter 19 - Security for the Internet of Things

To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system’s superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.

After the initial emergency responses deployed to control the coronavirus disease 2019 (COVID-19) pandemic in the first half of 2020, we should now start thinking about long-term strategies and concepts for pandemic and disaster governance such as resilience. In this context, COVID-19 health care and education are especially important because they are essential public goods that determine what kind of a society we live in, during the pandemic and afterward. So for, the focus has been a tactical efficiency perspective that prioritized instrumental, logistical, or pragmatic aspects in planetary health and university education, with much less attention paid to social justice, history of inequity, and power asymmetries that affect the pandemic impacts in society. For a resilient COVID-19 response, we need to address not only medical, technical, and logistical challenges, but also the social disparities that are inherited from the prepandemic world that are negatively affecting the current pandemic outcomes.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

ABSTRACTThe rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication and real‐time monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the context of blockchain‐based IoT systems. The study categorizes data aggregation algorithms into Privacy‐Preserving, Machine Learning‐Based, Hierarchical, Real‐Time, and Custom Aggregation Algorithms, each tailored to specific IoT requirements. Privacy‐Preserving Aggregation Algorithms focus on safeguarding sensitive data through encryption and secure protocols. Machine Learning‐Based Aggregation adapts dynamically to data patterns, offering predictive insights and real‐time adaptability. Hierarchical Aggregation organizes devices into a structured hierarchy, optimizing data processing. Real‐Time Aggregation processes data instantly, ensuring low latency for time‐sensitive applications. Custom Aggregation Algorithms are bespoke solutions tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques, this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research directions. The integration of blockchain‐based data aggregation techniques not only enhances IoT network efficiency but also ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 2022. Moreover, some important parameters such as privacy and security, latency, data processing, energy consumption, complexity, and reliability were involved in these investigations.

The demand of higher spectrum bandwidth can almost have mitigated now-a-days by the use of Internet of Things (IoT) based 5G communication networks. IoT is getting massive attention because of the complexity features of IoT devices. Its less power constraints property have decreased the utilization of traditional cryptographic methods though it has used the Trusted platform module (TPM) for authentication and Trusted network connect (TNC) standards to monitor the malicious software or firmware. This paper, proposed an algorithm based on Physical layer key generation (PLKG) and Physical layer encryption (PLE) as a protecting measures. Also this paper investigates the advanced encryption standard (AES) and make a comparative study to prove it's much secured, effective and fast encryption features among the rest of the available methods and concludes that AES produces a better solution.

Security of the data is a major challenge in the deployment and management of the Internet of Things (IoT) systems in the heterogeneous environment, where data gets transferred very widely and rapidly in multiple modes across the globe. Concomitant with the advancement of the technology, the number of devices and systems which are involved in transferring the image data has increased in the heterogeneous IoT environment. At present, image processing devices have become an integral part of the IoT edge devices which can collect the images and automatically share with the network. Because of the multifaceted usage of images in multimedia applications in the IoT environment, it is essential to protect the data from unauthorized access. The Advanced Encryption Standard (AES) is a well-established cryptography algorithm used in numerous applications to protect digital data. In this paper, we present a Hardware-Software Co-Simulation with Xilinx System Generator of an AES-128 bit encryption and decryption for IoT Edge devices. Hardware Co-Simulation makes it possible to integrate a design running in an FPGA directly into Simulink simulation. AES-128 bit algorithm has been implemented on the SPARTAN-6 (XC6SLX45-CSG324) FPGA board with XILINXISE software. Authors can get the successful results for image encryption and decryption both while implementing on grey and colored images. For Hardware Co-Simulation of AES-128 bit algorithm with Xilinx system generator, a point-to-point Ethernet approach is used. This implementation is done using Xilinx ISE14.2 and MATLAB 2011a for both grey image and colored image of size 448*298.

The efforts of medicine are gradually shifting from combating a specific disease to ensuring the individual well-being of patients with a simultaneous increase in the information capacity of medicine. Information and communication technologies are considered as a key factor in any strategy to improve the quality and cost-effectiveness of medical care. The development of electronic medical records, intelligent healthcare, mobile medicine, and artificial intelligence systems has opened up new opportunities for collaboration and interaction between healthcare providers and patients within the digital ecosystem of medical care, stimulating technological innovations. The medical ecosystem is designed to implement a systematic approach to the processing of clinical data as the basis for improving the effectiveness of medical care through the digital transformation of end-to-end medical technological processes. It focuses on the integration of the work of different providers and consumers of medical care, the exchange of data and information between them to provide comprehensive medical care to patients of a certain association of MO. Medical ecosystems are considered as systems that focus on data and the generation of new health-saving knowledge, that is, as open and loosely connected systems that allow participants to use the acquired knowledge in their own way, for example, in individual ecosystems of the participants’ MO. The digital transformation of healthcare is taking place in the wake of the digital economy. We are dealing with the transfer of innovative solutions of business ecosystems to medicine, which implies the need to take into account the specifics of the subject area. The key technology of digital transformation of medicine, following many sectors of the economy, is considered to be the digital twin method, the application of which is impossible without the use and development of other ecosystem technologies: EMC, big data analytics, AI, the Internet of things and blockchain. An effective and comprehensive implementation of the concept of digital twins for such a subject area as medicine is possible if the problem of combining providers and consumers of medical care into a digital medical ecosystem capable of providing holistic and homogeneous primary data is solved. The implementation of the concept of joint patient health management within the digital ecosystems of medical care faces obstacles, primarily due to complex issues related to interoperability, confidentiality, security and effective data management. The solution to possible problems lies in ensuring the priority of scientific research in order to strengthen the objectification of the choice of ecosystem technologies and determine the stages in achieving the set goals. The development of the ecosystem must be considered in the form of building blocks, which eventually overlap one another. The first, lower blocks are designed to expand the capabilities of existing technologies, and later ones will require special research in the field of information technology in a broad sense, as well as medical informatics, in particular. Proactive changes in the regulation of the industry are also needed, taking into account the challenges of the digital economy and the dynamics of changes in the social sphere.

Together with the popularity of internet of things (IoT), the information security in IoT is becoming an urgent issue. In this paper, a cryptographic coprocessor is designed to provide security for IoT sensor nodes. This design incorporates the application-specific instruction set to support popular cryptography algorithms like advanced encryption standard (AES) and elliptic curve cryptography (ECC). The tailored instruction provides good flexibility, high energy efficiency and low latency. Local instruction and data ram is integrated into our coprocessor to reduce the instruction transfer between embedded processor and the coprocessor. Our work was synthesized under TSMC 65 nm technology. The measurement results show that our design consumes 32.7 uJ for each ECC point multiplication and 3 nJ for each AES encryption and decryption when working at 10 MHz.