An Advanced Blockchain-Enabled Hybrid Cryptosystem for Protecting Sensitive Data in Cloud Environments

Cet article decrit, du point de vue d'un clinicien generaliste, une solution pour l'implementation de dossiers concernant les patients en toute securite, ainsi que les strategies mises en place pour cette implementation. Cet article souligne donc l'importance de la securite pour la confidentialite de ces dossiers, mais aussi la difficulte de mettre en place un systeme de protection de l'information sur

With the rapid growth of digital data and inter-connected devices, the integration of blockchain and Internet of Things technology offers a promising chance to revolutionize data management approaches. With today's global connectivity, the management and securing of ever-increasing amounts the amount of data produced by Internet of Things (IoT) devices is becoming an increasing concern. This study investigates a new solution to address such a concern by integrating blockchain technology into IoT systems. The aim is to develop a strong framework that takes advantage of the decentralized and secure character of blockchain and the wall-to-wall connectivity offered by the IoT. This integration is intended to address important needs such as data integrity, The integration aims to tackle crucial issues like data integrity, privacy, and unauthorized access. With the connectivity and sensing capabilities of the IoT, along with the blockchain technology of decentralized consensus and immutable ledgers, businesses are able to develop secure systems to retrieve, transfer, and store such data. In the experiments conducted on the Ethereum blockchain platform, it has been found that the blockchain and IoT technology solution not only secures data but also facilitates information management. The experiment results demonstrate that the proposed solution is able to protect against tampering, increase the transparency of transactions, and to minimize the chances of unauthorized access. The framework is scalable and highly adaptable, and it is therefore a promising solution for various applications. This framework employs an innovative approach to ensure secure and efficient data management.

This study develops and evaluates a secure webbased lecture delivery module designed for tertiary institutions in Kogi Central.The increasing concerns regarding unauthorized access, data breaches, and manipulation of academic records in conventional web-based platforms has become one of great concern. The system integrates students’ academic database with strong encryption schemes and blockchain techniques to ensure data confidentiality, integrity, and accountability. The research adopt the Secure System Development Life Cycle (SSDLC) while a multi-layered architecture that comprises of three layers was used.The proposed framework combines a userfriendly interface for lecture delivery with a backend architecture that secures student information through encryption and blockchainsupported logging. Empirical testing was conducted to assess system performance, security resilience, and scalability under realistic workloads. The findings demonstrate that the integration of encryption and blockchain enhances trust, prevents tampering, and ensures robust access control. This work contributes to the development of secure educational technologies for data-sensitive environments and provides a model that can be extended across similar academic settings

The integration of cloud computing into healthcare systems has revolutionized data management by enabling seamless data sharing and enhanced accessibility, thereby improving patient outcomes and operational efficiency. However, the sensitive nature of healthcare data necessitates robust privacy-preserving mechanisms to protect patient confidentiality. This paper explores state-of-the-art techniques such as cryptography, data anonymization, and secure access control, which are tailored to address privacy challenges in healthcare environments. It also critically evaluates existing frameworks, identifying limitations, and proposes a scalable architecture designed to ensure secure and efficient data sharing. Healthcare systems increasingly rely on cloud environments for storing and sharing sensitive patient data. However, these systems face significant challenges, including the risk of data breaches, unauthorized access, and the need for strict compliance with regulatory frameworks such as HIPAA and GDPR. To address these challenges, this paper presents a privacy-preserving framework that leverages advanced cryptographic methods, blockchain for secure transaction logging, and machine learning-based anomaly detection to safeguard patient data from collection to sharing. Experimental results demonstrate that the proposed system outperforms traditional methods in terms of privacy, efficiency, and scalability, showcasing its potential for real-world healthcare applications. The integration of cloud computing into healthcare systems has significantly transformed how patient data is managed and shared across multiple stakeholders, including healthcare providers, insurers, and researchers. This digital transformation has enabled real-time access to medical records, improving clinical decision-making and patient outcomes. However, the sensitive nature of healthcare data demands the adoption of advanced privacy-preserving techniques to mitigate risks such as unauthorized access, data breaches, and regulatory non-compliance. In this paper, we explore state-of-the-art technologies like cryptographic techniques (homomorphic encryption, differential privacy), data anonymization (k-anonymity, l-diversity), and access control mechanisms (RBAC, ABAC) that have been proposed to enhance data privacy and security. Additionally, we delve into the use of blockchain technology for secure, transparent transaction logging and machine learning-based anomaly detection systems to safeguard against unauthorized access. Through experimental analysis and performance testing, we demonstrate that the proposed privacy-preserving framework outperforms conventional methods in terms of scalability, efficiency, and data protection, thereby offering a promising solution for real-world healthcare applications.

The integration of Zero-Trust Architecture (ZTA) and Blockchain-based Security Models in IoT-driven industrial power electronics systems has emerged as a transformative approach to mitigating cyber threats and ensuring robust access control. Traditional security mechanisms, which rely on perimeter-based defenses, are increasingly ineffective against advanced persistent threats (APTs), insider attacks, and lateral movement techniques within industrial IoT (IIoT) environments. Zero-Trust security enforces continuous verification, least-privilege access, and micro-segmentation, ensuring that no device or user was inherently trusted. Implementing ZTA in resource-constrained IoT ecosystems presents significant challenges related to computational overhead, authentication latency, and secure data transmission. To address these limitations, blockchain technology enhances decentralized identity management, immutable access logs, and tamper-resistant security frameworks, fortifying Zero-Trust-based access control. Privacy-preserving cryptographic techniques, including zero-knowledge proofs (ZKPs) and homomorphic encryption, safeguard sensitive industrial data while maintaining compliance with evolving regulatory frameworks. AI-driven anomaly detection models reinforce continuous authentication and behavior-based threat monitoring, enabling proactive defense mechanisms against zero-day exploits and sophisticated cyber intrusions. This chapter presents a comprehensive analysis of Zero-Trust implementation models for IIoT systems, highlighting the role of secure communication protocols, distributed ledger-based identity verification, and adaptive security automation. The integration of blockchain-enabled access control and AI-powered real-time security analytics ensures a resilient security posture for industrial power electronics networks, mitigating risks associated with unauthorized access, data breaches, and operational disruptions. The proposed framework enhances scalability, privacy, and computational efficiency, paving the way for next-generation cybersecure industrial ecosystems.

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.

The digital landscape continues to evolve; ensuring robust internet safety has become a critical challenge in the face of growing cyber threats. This chapter delves into the transformative potential of artificial intelligence (AI) and blockchain technology in reinforcing cybersecurity measures. AI's ability to analyze large-scale data, identify patterns, and predict potential vulnerabilities makes it an invaluable tool in enhancing security protocols. When combined with blockchain's decentralized and immutable nature, which secures data by providing an incorruptible ledger, these technologies collectively offer an advanced framework for real-time threat detection and mitigation. The chapter explores several key applications, including decentralized identity management, cyber fraud detection, and privacy protection, illustrating how the fusion of AI and blockchain can help address significant security concerns such as data breaches, identity theft, and unauthorized access. However, the integration of AI and blockchain into cybersecurity systems is not without challenges. Issues such as scalability, energy efficiency, and the technical complexity of merging these technologies in practical settings remain obstacles. Despite these hurdles, significant advancements in blockchain protocols, such as sharding and AI-based optimization algorithms, show promise in overcoming these limitations through case studies and recent developments; this chapter demonstrates how AI and blockchain can work together to create a more secure, resilient, and transparent digital environment. This chapter is merely focused on the various technology aspects to secure date through various AI and blockchain techniques.

Security of Computer Networks

The article examines methods and information technologies aimed at ensuring the secure integration of the Ethereum blockchain with Internet of Things (IoT) systems. The relevance of the study is driven by the rapid development of IoT, which is accompanied by increasing cybersecurity threats, including unauthorized data access, man-in-the-middle (MITM) attacks, device identifier spoofing, and the low transparency of centralized systems. The use of Ethereum blockchain technology, particularly smart contracts, opens new opportunities for creating decentralized security management models for IoT devices, enhancing trust levels, automating processes, and minimizing third-party interference risks. The “Problem statement” section outlines the key challenges in securing IoT networks, including the vulnerabilities of centralized solutions, limited computational resources of devices, and the need to develop autonomous access control systems. The “Analysis of Recent Research and Publications” section summarizes modern approaches to integrating Ethereum blockchain into the IoT field, including tokenized identification mechanisms, access control, and transaction processing using smart contracts. It is noted that leading researchers suggest Layer-2 solutions (state channels, zk-rollups, Plasma) aimed at reducing the load on the main blockchain and improving scalability. The aim of the article is to systematize modern methods of integrating the Ethereum blockchain with IoT and develop recommendations for their implementation, considering the limited resources of devices. The “Research results” section presents a secure IoT system architecture concept based on decentralized account management, local storage of cryptographic keys on devices, the use of optimized transaction signing algorithms, and the introduction of a hybrid data storage model based on IPFS. The proposed model minimizes the risk of unauthorized access, increases transparency in the interaction between IoT devices, and reduces computational resource costs. The “Conclusions and prospects for further research” section emphasizes that the implementation of the Ethereum blockchain in IoT promotes the development of secure decentralized platforms. However, it requires addressing issues such as energy consumption, transaction costs, and optimizing client applications for low-performance devices. Future research should focus on developing effective scalability tools, adapting smart contracts to IoT specifics, and improving integration with cloud computing platforms for storing large data sets.

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

The secure management of healthcare and education systems has become an essential component in ensuring data privacy, trust, and transparency in handling sensitive information. Healthcare systems manage vast amounts of personal medical data, while education systems handle confidential student records and academic results. Both sectors rely on secure methods to ensure that data is properly handled, shared, and stored. Historically, centralized databases were used to manage these systems, offering control but leaving vulnerabilities, such as data breaches, unauthorized access, and manipulation of information. Many organizations used centralized security protocols like firewalls, encryption, and access control systems, but they often fell short of providing full security due to single points of failure. The integration of Blockchain technology offers a decentralized solution, eliminating central control and providing a secure, transparent, and immutable way to manage sensitive data. Blockchain’s unique ability to record and verify transactions on a distributed ledger ensures that once data is entered, it cannot be altered, reducing the risk of fraud or errors in both healthcare and education records. The motivation behind implementing Blockchain in these sectors is to address the security risks and inefficiencies of centralized systems, offering a solution where all stakeholders can have a clear, tamper-proof view of the data. The primary problems faced with centralized management include vulnerabilities to hacking, data loss, and the complexity of audits. The proposed system, utilizing Blockchain, ensures decentralized data storage, enhanced privacy, and transparent access management, fostering a trusted environment for both healthcare and education systems. By using smart contracts and cryptographic security, the integrity of healthcare and education data is guaranteed, ensuring that all stakeholders—from patients to students, and from educators to healthcare providers—can trust the system’s reliability and security.

Purpose & Objective: With the increasing digitization of healthcare data, ensuring the privacy, security, and compliance of Protected Health Information has become a critical challenge. This paper presents a Machine Learning (ML)-based governance algorithms designed to enhance the protection of data by identifying vulnerabilities, detecting anomalies, and ensuring regulatory compliance. The objective is to develop an intelligent, adaptable, and automated system that strengthens healthcare data governance while minimizing risks associated with unauthorized access and data breaches. Methodology: The proposed algorithms integrate ML-driven algorithms for real-time monitoring, anomaly detection, and predictive risk assessment. Multiple algorithms, including supervised and unsupervised learning models, have been implemented to classify potential threats and unauthorized access patterns. The model has been trained on diverse datasets to enhance accuracy and adaptability. A rule-based governance layer has been incorporated to ensure compliance with healthcare data protection regulations. The implementation phase involves testing the model effectiveness in real-world healthcare environments, evaluating its accuracy, efficiency, and scalability. Outcomes: The experimental results demonstrate that the proposed ML-based governance algorithms significantly improve the security and privacy of PHI. The system successfully detects anomalies with high accuracy, reduces false positives, and ensures data integrity through automated policy enforcement. The findings indicate that ML-driven governance can effectively mitigate risks, enhance compliance, and optimize healthcare data management by proactively addressing security concerns. Limitations & Future Scope: Despite its promising results, the proposed algorithm has certain limitations, including dependency on high-quality training data, potential biases in ML models, and computational overhead in large-scale deployments. Future research will focus on refining algorithmic efficiency, integrating federated learning for enhanced privacy, and expanding the model to accommodate evolving data governance regulations. Incorporating explainable AI techniques will improve transparency and trust in automated decision-making processes within healthcare data governance. Index terms Machine Learning (ML), Protected Health Information (PHI), Data Governance, Privacy Protection

: Data processing, management, and storage have all been changed by the internet of things, which offers companies scalable and reasonably priced solutions. There is serious cybersecurity concerns connected to using cloud services, such as service outages, unauthorized access, and data breaches. Investigating these security threats and developing defenses to enhance cloud security are the goals of this study. This study reviews current security mechanisms including encryption, authentication, and access control and highlights critical vulnerabilities in cloud systems through methodical literature analysis and risk assessment. To assist enterprises in evaluating and prioritizing cloud security concerns, the study suggests a risk assessment methodology. To strengthen cloud computing's overall security posture, best practices and security solutions are also advised. The results of this study will help create cloud infrastructures that are more secure, guaranteeing data availability, confidentiality, and integrity in the rapidly changing digital environment.

Purpose: This paper proposes an adaptive security model designed for cloud platforms, integrating information security principles and cryptographic protocols to address evolving cybersecurity threats. The model ensures dynamic security control adjustments based on real-time risk assessments to protect enterprise applications and business operations. Methodology: The proposed model employs continuous monitoring, intelligent threat detection, and automated responses to proactively mitigate risks. It integrates cryptographic techniques such as AES, RSA, and elliptic-curve cryptography to secure data transmission, storage, and access control. Additionally, machine learning-driven anomaly detection and behavioral analytics dynamically refine security policies. Findings: The model enhances cloud security resilience against data breaches, unauthorized access, and service disruptions. By leveraging automated security orchestration, it ensures scalability, resilience, and operational efficiency while minimizing system overhead. The experimental implementation confirms its effectiveness in mitigating threats while maintaining high system performance and availability. Unique Contribution to Theory, Practice, and Policy (Recommendations): This research contributes to cloud security advancements by presenting a comprehensive, adaptable security framework that addresses both known and emerging attack vectors. It ensures compliance with industry regulations such as GDPR, HIPAA, and SOC 2, providing organizations with a structured approach to meeting evolving cybersecurity mandates. Future research should explore enhanced AI-driven security orchestration, quantum-resistant cryptographic protocols, and cross-cloud security interoperability to further fortify cloud infrastructures.

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.