The accelerating pace of adoption of decentralized applications requires the use of efficient, high-performance blockchain infrastructures. These blockchains are supported by consensus algorithms that are critical determinants of scalability, transaction speed, costs, and security. Developers are still unaware of the most useful options because there is a large gap in information regarding the comprehensive evaluation of these algorithms in practice. This study aims to test and compare the performance of the three most popularly used blockchain consensus protocols - Proof of Work (PoW), Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) with the aim to improve the application of decentralized systems. By deploying smart contracts on real blockchain test networks (Callisto for PoW, Sepolia for PoS, and Tron Nile for DPoS), the research explores key performance metrics such as block time, deploy gas fee, block gas limit, and block size. The experiments utilize tools like Remix IDE and Tron-IDE, highlighting the practical implications of consensus algorithms under varying real-world conditions, including network congestion and transaction volatility. Tron Nile excels in speed and capacity but incurs high gas fees, Sepolia balances performance with moderate costs, and Callisto emphasizes cost efficiency at the expense of speed and scalability. Insights derived from this study provide valuable guidelines for developers to choose suitable consensus mechanisms based on the specific requirements of decentralized applications
Large quantities of processing resources with strict latency specifications are needed for Internet ofThings (IoT) devices due to the rise of compute-intensive and delay-sensitive mobile apps. One promising solutionis to transfer resource-intensive computational tasks from IoT devices to either edge computing servers or cloud computing servers. This paper aims to apply a simplified distributed ledger to an edge network to follow up the offloaded data and maintain the response time as much as possible. The voting process is used as a consensus to validate the new block, while the offloading decision is based on a fixed processing time offloading threshold value. The proposed model has been programmed and the experimental evaluation of the proposed model shows that the ledger did not significantly lengthen the response time and the offloaded task has been successfully tracked
The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things (IoT) devices. This article addresses the privacy and security issues brought up by data sharing in the context of IoT fog computing. The suggested framework, called “BlocFogSec”, secures key management and data sharing through blockchain consensus and smart contracts. Unlike existing solutions, BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing, while employing a consensus protocol to validate transactions and maintain blockchain integrity. To process and store data effectively at the network edge, the framework makes use of fog computing, notably reducing latency and raising throughput. BlocFogSec successfully blocks unauthorized access and data breaches by restricting transactions to authorized nodes. In addition, the framework uses a consensus protocol to validate and add transactions to the blockchain, guaranteeing data accuracy and immutability. To compare BlocFogSec's performance to that of other models, a number of simulations are conducted. The simulation results indicate that BlocFogSec consistently outperforms existing models, such as Security Services for Fog Computing (SSFC) and Blockchain-based Key Management Scheme (BKMS), in terms of throughput (up to 5135 bytes per second), latency (as low as 7 ms), and resource utilization (70% to 92%). The evaluation also takes into account attack defending accuracy (up to 100%), precision (up to 100%), and recall (up to 99.6%), demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.
Because block chain technology can improve distributed systems' security, dependability, and resilience, it has been gaining popularity. Research based on this technique has helped a number of fields, including data analysis, finance, remote sensing, and healthcare. The primary characteristics that make block chain technology appealing include distributed ledgers, decentralization, privacy, transparency, and data immutability. However, because there is a chance of a privacy breach, medical records that hold private patient information make this system extremely complex. The purpose of this project is to investigate block chain applications in the healthcare sector. We also include articles that touch on other topics, like the Internet of Things, information management, medicine supply chain tracking, and privacy and security issues. Lastly, we aim to investigate block chain concepts in the medical field by evaluating their advantages and disadvantages and providing direction to other studies in the field. We also provide a summary of the Block chain's techniques. Index Terms—IoT, Block Chain, Healthcare, Smart Contract, Etherum.
Nassmah Y. Al-Matari, Ammar T. Zahary, Asma A. Al-Shargabi
The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities in spectrum access and security. Blockchain technology has emerged as a viable solution to address these challenges, offering enhanced security, transparency, and efficiency in spectrum management. This survey paper offers a thorough analysis of recent advancements in blockchain-enabled security mechanisms specifically for spectrum access within 6G cognitive radio IoT networks. Covering literature from 2019 to the present, the paper highlights significant contributions and developments in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access security and shows how blockchain's decentralized approach can solve related issues. Key areas of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized databases, and smart contracts for spectrum management. The paper also addresses ongoing challenges like interoperability, scalability, and the need for comprehensive security frameworks. Future research directions are proposed, emphasizing the development of advanced blockchain protocols, integration with machine learning, and addressing regulatory and standardization concerns. This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain technology, alongside ML/AI, to enhance security and efficiency in next-generation cognitive radio IoT networks.
The integration of Blockchain technology into Internet of Things (IoT) ecosystems has emerged as a transformative approach to address critical security and privacy challenges in connected devices. This chapter explores the innovative convergence of Blockchain and IoT, focusing on the enhancement of data security, privacy, and trust through decentralized solutions. By leveraging the immutable and transparent nature of Blockchain, IoT networks can achieve secure, efficient, and automated data exchanges. The chapter delves into key Blockchain trends that are shaping IoT security, including decentralized identity management, smart contracts, and consensus mechanisms, while addressing the specific challenges of resource-constrained IoT devices. The use of advanced cryptographic techniques such as encryption, Zero-Knowledge Proofs, and homomorphic encryption ensures that sensitive data remains private and secure. The application of Blockchain in facilitating secure data sharing, device authentication, and access control was discussed, alongside the potential of permissioned blockchains to balance privacy with scalability. The chapter concludes by identifying the future directions for Blockchain and IoT innovations, emphasizing their role in the evolution of secure, autonomous, and scalable connected environments.
Blockchain and artificial intelligence are two of the most prominent technologies in computer science today and have attracted considerable attention from various research communities. Recently, several initiatives have been launched to explore the combination of these two pioneering technologies. The main goal is to combine the data integrity, privacy, and decentralization properties of blockchain with the ability of artificial intelligence to process, analyze, predict, and refine massive data sets. The combination of blockchain and AI technologies is expected to address key challenges in the digital realm, such as data security, transparency, and streamlined decision-making. However, there is a problem that many studies have focused on the advancement of a single technology as the main perspective. To overcome these recent research limitations, we provide a broad view of the combination of blockchain and artificial intelligence and analyze the limitations of existing research and their causes. Furthermore, we identify challenges and attempts to be addressed through this analysis. The analysis in this paper is organized into a comprehensive section dedicated to the application of artificial intelligence in blockchain and vice versa. Based on our analysis, we identify existing challenges and propose a novel framework for researchers to overcome these limitations, thus expanding new research opportunities.
This chapter explores the critical intersection of consensus mechanisms and performance metrics in blockchain systems for the Internet of Things (IoT). As IoT networks expand, the demand for scalable, efficient, and secure blockchain solutions intensifies. This chapter evaluates the trade-offs between energy efficiency, latency, scalability, and security within IoT-integrated blockchain frameworks. Key consensus protocols such as PoW, Proof of Stake (PoS), and Delegated DPoS) are analyzed for their applicability in IoT environments, highlighting their impact on transaction speed, resource utilization, and network resilience. Hybrid consensus models are examined for their potential to balance performance with robust security. The chapter also provides real-world case studies that benchmark latency and energy efficiency, offering insights into the practical challenges and opportunities for IoT-based blockchain applications. Key findings contribute to the ongoing development of optimized blockchain solutions for IoT networks.
Safa Hussein Oleiwi, Saraswathy Shamini Gunasekaran, Karrar Ibrahim Abdulameer, Mazin Abed Mohammed · 5 authors
The increasing number of Internet of Things (IoT) devices in healthcare applications, particularly during emergencies, necessitates safe protocols for transmitting real-time data. Medical data are essential for healthcare applications, and reliance on IoT devices to control information flow necessitates the consideration of five critical areas. This work addresses the security challenges associated with the transmission and storage of copyrighted healthcare data, as well as the inadequacy of the present methods in facilitating real-time data transfer given the volume of data and network conditions. This research provides a theoretical framework for the secure and immediate offloading of computations in IoT healthcare systems. The objective is to implement secure communication and networking technologies to ensure the security and integrity of medical data, maintain confidentiality, and facilitate real-time transmission of information. The proposed framework is simulated in MATLAB for system model implementation. A blockchain network sandbox was established with the delegated proof-of- stake (DPoS) consensus method, supplemented by proof-of-work (PoW) and proof-of-validation (PoV) for enhanced security. To assess the efficacy of this framework, multiple test scenarios focused on the number of nodes, the volume of data, and the conditions of network connectivity. The results demonstrated the system's efficacy in facilitating the offloading of real-time data in IoT healthcare applications. The aforementioned study demonstrated that the framework exhibited rapid transaction processing, efficient resource use, and energy conservation while also enhancing secure data transmission across various network conditions. The findings confirm that the proposed architecture can effectively and securely transmit real-time data in IoT healthcare applications without jeopardizing data authenticity, privacy, or integrity. The system's ability to address security challenges and manage substantial data volumes under varying settings indicates that it can be effectively deployed in healthcare systems, particularly in critical situations.
Christian Delgado‐von‐Eitzen, Luis Anido, María Ruiz‐Molina, Manuel J. Fernández Iglesias
ABSTRACT Introduction The popularization of blockchain‐based applications made evident a critical challenge, namely the inherent isolation of these decentralized systems, akin to the disconnected and technologically diverse local area networks of the 1970s. This lack of interoperability limits the potential for widespread adoption and innovation in the blockchain space. While various initiatives aim to bridge this gap, many remain nascent. Methods This article addresses this issue by proposing a robust architecture and practical implementation to interconnect two Ethereum‐based blockchains, enabling seamless smart contract interactions across these chains, and facilitating the exchange of complex information beyond mere token transfers. Results Our work explores the emerging landscape of inter‐blockchain communication, highlighting their current maturity and potential, and providing insights on how to overcome the technical hurdles associated with these protocols, particularly in the context of transmitting complex data and executing cross‐chain function calls. Additionally, we illustrate with a case study the challenges posed by linking private blockchains with public ones, ensuring secure and efficient data exchange. Conclusion This article aims to inspire blockchain researchers and practitioners, presenting a foundational framework for enhancing blockchain interoperability, including detailed, practical steps for its implementation. By laying the groundwork for more connected blockchain ecosystems, we intend to support the continued evolution and widespread adoption of blockchain technology.
The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.
Article investigates a blockchain-based framework for enhancing data security in Internet of Things (IoT) systems. Employing a qualitative research methodology, the study explores the integration of blockchain technology to address vulnerabilities in IoT ecosystems, including data breaches, unauthorized access, and the challenges of centralized data storage. By analyzing existing literature, case studies, and expert opinions, the research identifies blockchain's potential to provide secure, decentralized, and immutable data management in IoT systems. The findings highlight blockchain's ability to enhance data integrity through distributed ledgers, ensure data confidentiality via advanced cryptographic techniques, and improve accountability with transparent transaction records. Additionally, the research underscores the scalability challenges of blockchain in IoT, proposing hybrid architectures that combine private and public blockchain systems to optimize performance and resource utilization. Real-world applications such as smart home systems, healthcare IoT, and industrial IoT demonstrate the practical viability of blockchain integration for improving security. The study also emphasizes the importance of regulatory frameworks and cross-industry collaboration to address interoperability and privacy concerns. This research contributes to the growing discourse on secure IoT infrastructure by presenting a comprehensive blockchain-based security framework. The proposed framework offers actionable insights for IoT developers, researchers, and policymakers seeking to enhance trust, reliability, and resilience in IoT systems.
Alaa Awad Abdellatif, Khaled Shaban, Ahmed Massoud
This study introduces a secure, adaptable, and decentralized learning framework empowered by blockchain technology to enhance smart grid security and efficiency. Security is achieved through blockchain’s ledger, ensuring data integrity, privacy, and resilience. Adaptability refers to the framework’s ability to adjust to changing conditions, supporting multiple learning paradigms . Decentralization enhances fault tolerance by distributing control across nodes. Our framework excels in scalability, data-exchange security, and rapid response times , aiming to establish an intelligent blockchain-based smart grid supporting centralized learning (CL), federated learning (FL), and active federated learning (AFL). We present an innovative blockchain-based architecture customized to optimize information sharing and security within the blockchain. Our solution addresses various learning paradigm requirements by: (i) Selecting reliable entities for participation based on high-quality training data models; (ii) Acquiring a reliable subset of data for CL and AFL, balancing learning performance , latency, and cost; (iii) Adjusting blockchain configuration to align with specific learning paradigm requirements. Results from real-world datasets demonstrate superior performance compared to existing solutions. Our framework achieves high learning performance while minimizing latency and blockchain costs.
Tiago Guimarães, Ricardo Duarte, Francini Hak, Manuel Filipe Santos
Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.
Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón · 5 authors
This paper evaluates the technical feasibility of blockchain technology within the healthcare ecosystem, with a focus on the use of Corda Distributed Ledger Technology (DLT) to ensure data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity—ensuring that transmitted data remains unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda Network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda blockchain technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.
Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.
The rapid deployment of 5G networks necessitates the development of secure, scalable, and efficient Internet of Vehicles (IoV) systems. Existing IoV solutions often struggle with real-time threat detection, scalability, efficient resource allocation, and privacy preservation. This work proposes an integrated framework leveraging blockchain technology, AI-driven anomaly detection, dynamic network slicing, and secure multi-party computations. We introduce AI-Driven Anomaly Detection and Mitigation (ADAM) to identify and respond to security threats in real-time. Utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), ADAM analyzes network traffic data to detect anomalies with a detection accuracy of 95%, a false positive rate of 2%, and an average response timestamp of 50 ms. To tackle scalability and latency issues inherent in traditional blockchain systems, we propose Edge-Based Blockchain Sharding (EBBS).The innovative use of a modified Proof-of-Stake (PoS) mechanism tailored for edge environments further enhances the scalability of the IoV system. AI-Enabled Dynamic Network Slicing (ADNS) is implemented to optimize resource allocation based on real-time traffic demands and QoS requirements. Finally, we incorporate Secure Multi-Party Computation for Collaborative Data Processing (SMPC-CDP) to enable secure, privacy-preserving data analysis among IoV entities ensuring privacy with a computation overhead of 20%, and data utility preservation of 95%.
In the Web3.0 era, which does not rely on any centralized organization and emphasizes user control, security, trustworthiness and the importance of data privacy, blockchain plays a key role. Its decentralization, security and trustworthiness and other characteristics have become Building the infrastructure of trusted interconnection and value interconnection in the Web3.0 era has laid the foundation for the development of Web3.0. With the development of Web3.0, blockchain technology itself is also continuing to develop. There are more and more researches on the integration and innovative development of blockchain technology with big data, artificial intelligence, metaverse, Internet of Things and privacy computing. In this context, the basic principles and characteristics of Web3.0 and blockchain technology are first explained, focusing on the decentralization, traceability and non-tampering characteristics of blockchain, and then an overview of the relationship between blockchain and The advantages of the integrated innovation and development of big data, artificial intelligence, metaverse, Internet of Things and privacy computing are analyzed.The standardization construction situation and future work prospects of the integrated innovation and development of blockchain technology under Web3.0 are analyzed.
Blockchain and cloud technologies together constitute a major breakthrough in distributed systems architecture since they provide companies with unheard-of security, scalability, and operational efficiency. The symbiotic relationship between blockchain's immutable ledger system and cloud computing's scalable infrastructure is investigated in this article together with how their convergence opens fresh opportunities for business applications in supply chain management, financial services, and healthcare sectors. By means of the analysis of important architectural patterns, including Blockchain-as-a-Service (BaaS) and hybrid storage solutions, it shows how this integration addresses conventional constraints while enabling creative features such as automated compliance, real-time settlement systems, and improved data management. The article also looks at technical issues in security, interoperability, and scalability and suggests ways to use the capabilities of both technologies. Emerging technologies like artificial intelligence and IoT combined with evolving industry standards point to a transforming effect on how companies handle and use data in the digital economy as this paradigm develops.
Abdullah Ayub Khan, Asif Ali Laghari, Abdullah M. Baqasah, Rex Bacarra · 7 authors
The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.
5G networks provide secure and reliable information transmission services for the Internet of Everything, thus paving the way for 6G networks, which is anticipated to be an AI-based network, supporting unprecedented intelligence across applications. Abundant computing resources will establish the 6G Computing Power Network (CPN) to facilitate ubiquitous intelligent services. In this article, we propose BECS, a computing sharing mechanism based on evolutionary algorithm and blockchain, designed to balance task offloading among user devices, edge devices, and cloud resources within 6G CPN, thereby enhancing the computing resource utilization. We model computing sharing as a multi-objective optimization problem, aiming to improve resource utilization while balancing other issues. To tackle this NP-hard problem, we devise a kernel distance-based dominance relation and incorporated it into the Non-dominated Sorting Genetic Algorithm III, significantly enhancing the diversity of the evolutionary population. In addition, we propose a pseudonym scheme based on zero-knowledge proof to protect the privacy of users participating in computing sharing. Finally, the security analysis and simulation results demonstrate that BECS can fully and effectively utilize all computing resources in 6G CPN, significantly improving the computing resource utilization while protecting user privacy.
Andreas Polyvios Delladetsimas, Stamatis Papangelou, Elias Iosif, George M. Giaglis
This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment.
Rasha Halim Razzaq, Mishall Al-Zubaidie, Rajaa Ghali Atiyah
Protecting patient data in the Internet of Medical Things (IoMT) is one of the major challenges facing healthcare organizations because of increasing threats to privacy and security. Although there are many existing protocols and solutions, such as Rivest–Shamir–Adleman (RSA) and El-Gamal cryptographies or centralized methods, that aim to protect data, they suffer from weaknesses such as slow performance or inability to handle large volumes of data. The issue of security in medical records has become an urgent need, and the use of centralized methods can expose them to single-point failure. In this paper, we present the efficient approach to securing patient information (EASPI), which depends on blockchain and integrates innovative techniques such as the advanced encryption algorithm (AES), reverse word frequency analysis (TF-IDF), Lemplel-Ziv-Welch (LZW), decision tree model (DTM), and naive Bayes classifier (NBC). EASPI seeks to improve the security of medical data by storing it encrypted and securely via blockchain technology, providing a high level of privacy and reliability. The experimental results indicate that the EASPI reduces the encryption execution time to 0.2 ms and the decryption execution time to 0.3 ms while improving the accuracy of medical diagnosis. The potential of the suggested methods for healthcare systems is further demonstrated by the fact that the TF-IDF algorithm attained an execution time of 0.004 ms, while the blockchain's greatest execution time was 0.014 ms. Additionally, using the formal verification Scyther tool, the security of the suggested system is examined both theoretically and practically. The suggested solution is an appropriate option for healthcare institutions since it offers a strong defense against a range of cyber threats, including targeted and espionage assaults.