Secure Data Management Research Articles

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

A Blockchain and Hybrid Deep Learning for Secure and Efficient Healthcare Data Transmission and Management

The Impact of Digital Technologies on Formative Assessment and the Learning Experience

The integration of digital technologies into formative assessment has revolutionized modern education by enhancing the learning experience and transforming how educators evaluate student progress. Digital tools such as Learning Management Systems, Artificial Intelligence-driven platforms, and immersive technologies like Virtual Reality (VR) and Augmented Reality (AR) provide real-time feedback, personalized learning pathways, and increased engagement. These innovations support diverse learning styles and foster deeper understanding through interactive and multimedia-rich environments. However, challenges such as the digital divide, lack of teacher training, ethical concerns surrounding data privacy, and biases in AI-driven systems pose significant barriers to equitable implementation. Future trends, including predictive analytics, blockchain for secure data management, and gamified assessment strategies, offer promising avenues for further development. Addressing these challenges while leveraging technological advancements can lead to a more inclusive, engaging, and effective educational landscape, preparing students for the demands of a rapidly evolving world.

An Integrated Operations Management System for Enhancing Administrative Efficiency in Ghana's District Assemblies

The COVID-19 pandemic, driven by the SARS-CoV-2 virus, necessitated an unprecedented shift to remote work, revealing critical gaps in digital infrastructures capable of maintaining organizational operations. This challenge was particularly pronounced in district assemblies in Ghana, where traditional, paper-based workflows impeded the transition to remote environments, heightening operational vulnerabilities. Despite the World Health Organization (WHO) downgrading COVID-19 as a Public Health Emergency of International Concern (PHEIC), the global ramifications of the virus and potential for future pandemics underscore the need for resilient, adaptable digital solutions. This paper presents an online integrated business operation management system tailored for district assemblies in Ghana. The system was developed using the Rapid Application Development (RAD) methodology, facilitating rapid, iterative deployment and user-centric design. Technologies such as HTML, CSS, and PHP were utilized for the front-end, while MySQL ensured secure and efficient back-end data management. The approach helped to digitize core administrative processes and transition from paper-based operations to streamlined electronic workflows. The implemented system successfully digitalized essential administrative workflows, improving operational continuity and efficiency. By replacing conventional procedures with robust electronic processes, the system provides a sustainable framework for enhanced functionality in public sector management. Furthermore, it strengthens organizational preparedness for future disruptions, ensuring resilience in remote and hybrid work contexts. The scope of this research is limited to district assemblies in Ghana, which may constrain the generalizability of the findings to other administrative contexts or geographical regions. Further studies could explore customization for broader governmental structures and assess long-term impacts on service delivery and user satisfaction. This paper presents a novel application of digital transformation principles within the public sector, addressing a critical gap in e-governance solutions for district assemblies in Ghana. By integrating user-focused design and scalable technology, the proposed system not only meets current administrative demands but also positions district assemblies for enhanced functionality and resilience in the face of future challenges.

Data Security and Access Rights Management on Anime Content Management Information System

In the increasingly developing digital era, data security and access rights management have become crucial aspects in information systems, especially on platforms that manage specific content such as anime. The Anime Content Management Information System (SIPKA) requires a robust security mechanism to protect user data, content and access rights in order to prevent security threats such as unauthorized access, data theft and content manipulation. This study discusses various methods and technologies that can be applied to improve data security and access rights management in SIPKA. Through literature analysis and case studies, this research found that implementing data encryption, multifactor authentication, and role-based access rights management (RBAC) systems are effective strategies in maintaining data integrity and confidentiality. It is hoped that the results of this research can provide guidance for SIPKA developers and managers in implementing appropriate security policies and technology to ensure the security and operational continuity of the system.

Exploring the integration of blockchain technology in healthcare monitoring systems for enhanced security and data integrity of patient information

The rapidly evolving healthcare landscape has intensified the focus on the integrity and security of patient data, raising critical concerns about safeguarding sensitive information. This research paper investigates the integration of blockchain technology within healthcare monitoring systems, aiming to establish secure and tamper-proof patient data management frameworks. By harnessing the decentralized and immutable characteristics of blockchain, this study explores the potential to enhance data security, streamline patient information access, and ultimately improve health outcomes. The methodology adopts a mixed-methods approach that includes a comprehensive literature review, case studies of current blockchain applications in healthcare, and expert interviews. Findings indicate that integrating blockchain can significantly reduce risks associated with data breaches and unauthorized access, thereby fostering increased trust between patients and healthcare providers. This paper contributes to the ongoing discourse on innovative technological solutions for protecting patient information and offers a foundation for future research in this critical area.

Assessing of software security reliability: Dimensional security assurance techniques

Security plays a crucial role in ensuring the reliability of software systems, making a secure and dependable security framework vital for safeguarding software integrity. However, evaluating the dynamic and multifaceted aspects of security presents significant challenges, as various security metrics and factors complicate the assessment of reliability. This study advances the core concepts of software security through the application of security assurance techniques, including vulnerability scanning, code review, penetration testing, threat assessment, control evaluation, mitigation, risk assessment, and configuration review. In the context of the Software Security Reliability Model (SSRM), a framework was developed to enhance software security assurance across different stages. A comprehensive systematic literature review was conducted to identify security challenges, and the STRIDE and DREAD methodologies were applied to model security threats effectively. Additionally, a mathematical CVSS scoring method was utilized for risk assessment. The synthesis of diverse security methods, tools, attack patterns, and systems was analyzed, identifying 15 critical software security terms: authentication, authorization, encryption, access control, network security, application security, data security, incident response, compliance, threat intelligence, privacy protection, third-party risk, cloud security, endpoint security, and identity management. The findings highlight these terms as key contributors to improving software security reliability.

Blockchain Implementation for Data Security in Digital Food Courts: Examining Socio-Economic and Environmental Impacts in the Case of Kestalan’s Digital Transformation

Blockchain technology has emerged as a transformative solution for enhancing data security in various sectors, including retail sales operations. This study explores its implementation in flexible food courts as part of digital transformation initiatives, focusing on bolstering data security, improving transparency, and streamlining sales processes. Blockchain's decentralized, immutable, and encrypted ledger addresses issues like data manipulation, fraud, and cybersecurity risks, making it particularly suitable for food courts adopting digital payment systems. Using a descriptive case study approach, the research employs interviews, direct observation, and literature reviews to examine blockchain's role in managing digital transactions, cashless payments, and real-time inventory tracking. The findings reveal that blockchain enhances data security by preventing unauthorized access, expediting transactions, and fostering customer trust through transparent, tamper-proof records. It also reduces operational inefficiencies and costs associated with data management and fraud prevention. However, challenges such as high implementation costs and a steep learning curve hinder widespread adoption. Despite these barriers, blockchain provides a robust framework for supporting digital transformation in flexible food courts, particularly among small and medium enterprises (SMEs), by ensuring secure, efficient, and transparent data management. The study recommends developing simpler, more affordable blockchain solutions tailored to SMEs and offering user training programs to facilitate smoother adoption. These advancements could pave the way for broader implementation, enhancing operational efficiency and fostering trust in digital ecosystems.

Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies

ABSTRACTConsumer Internet of Things (CIoT) has revolutionised agriculture by integrating intelligent computing, artificial intelligence and big data technologies in crop monitoring. This paper explores the application of intelligent computing and deep learning methodologies in crop monitoring within the CIoT framework. In CIoT‐based crop monitoring, a vision sensor collects real‐time data from crop leaf images. The image dataset is processed using state‐of‐the‐art deep learning models and intelligent computing algorithms. This integration enables the early detection of crop diseases by leveraging computer vision and deep learning. Intelligent computing systems provide accurate disease classification, real‐time alerts, and actionable recommendations for optimised crop management practises. This advanced system empowers farmers to make data‐driven decisions, such as irrigation optimization, targeted pesticide application and nutrient supplementation, to maximise crop productivity and minimise losses. A benchmark dataset of leaf images is used, and a deep learning based model is presented for classifying healthy and diseased leaves. Experimental results demonstrate an accuracy rate of 0.98, with detailed validation, including dataset size and model parameters. Key benefits of intelligent computing in CIoT‐based crop monitoring include enhanced resource efficiency, reduced environmental impact, and improved sustainability. The paper also addresses the challenges of implementing AI and big data technologies, such as data privacy, security, interoperability and resource management in agricultural settings.

The Impact of AI on the Development of Manufacturing, Logistics, and Financial Company from Capital Market Perspectives

Abstract: This paper explores the impact of AI technology on enterprises, especially in finance and capital markets. With the rapid development of AI technology, enterprises hope to improve productivity, financial management, and market competitiveness through AI, and ultimately achieve sustainable development. The article aims to analyze the application cases of AI in head enterprises and their effects and put forward corresponding suggestions and insights. By analyzing case studies of enterprises such as Tesla, Amazon and JP Morgan, it elaborates on the application of AI in various areas such as enterprise manufacturing, supply chain management, automated trading, risk management and customer service. It reveals the great potential of AI in improving enterprise operational efficiency, reducing costs and optimizing decision-making. By analyzing the above cases, it is suggested that enterprises should strengthen data privacy and security management and improve technology transparency and interpretability when introducing AI technology. The study reveals that AI technology has potential but also faces challenges, and enterprises need to keep overcoming obstacles to achieve the wide application and deep integration of AI technology.

Applied Research on the Impact of Big Data, Cloud Computing and Cloud Storage on Smart Manufacturing Supply Chain

The rapid advancements in big data, cloud computing, and cloud storage technologies are profoundly reshaping the operational models of smart manufacturing supply chains. The integration and application of these technologies not only drive the enhanced data collection and analysis capabilities across various supply chain segments but also significantly improve the accuracy of demand forecasting, the rationality of resource allocation, and the scientific nature of decision support. Consequently, the collaborative efficiency and dynamic response capabilities of supply chains have been substantially elevated, providing robust technological backing for enterprises in navigating the intricately volatile market environment. Nonetheless, while reaping the benefits of these technological advancements, challenges such as data security and privacy protection, system integration, and technical selection cannot be overlooked. By judiciously selecting technology platforms, reinforcing data security management, and continuously investing in technological advancements and talent cultivation, the holistic effectiveness of smart manufacturing supply chains will be further optimized.

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Understanding Multi-Cloud and Hybrid Cloud Architectures in Data Management

This comprehensive article explores the growing adoption of multi-cloud and hybrid cloud architectures in modern data management practices. It delves into the benefits, challenges, and best practices associated with these complex cloud strategies. The article presents key statistics and projections from industry leaders, highlighting the rapid growth and increasing importance of multi-cloud and hybrid cloud solutions. It discusses how these architectures enhance data redundancy, vendor flexibility, cost optimization, and performance while addressing critical issues such as data sovereignty, scalability, and legacy system integration. The piece also examines the challenges organizations face in implementing these strategies, including data integration, security, skill gaps, compliance, and cost management. Finally, it outlines best practices for effective implementation, emphasizing the need for comprehensive data governance, robust integration tools, cloud-agnostic technologies, and continuous optimization.

Bridging the gap: opportunities, challenges, and future directions for teleradiology in rural healthcare

Teleradiology, the electronic transmission of radiological images and data for diagnostic purposes, holds significant promise for improving healthcare access in rural and underserved regions. This review explores the opportunities and challenges of implementing teleradiology in rural settings, where limited access to radiology services exacerbates healthcare disparities. Teleradiology offers multiple benefits, including improved access to timely diagnostics, enhanced decision-making, and cost savings for rural patients who would otherwise need to travel to urban centers for radiology services. It has shown positive outcomes globally, with examples in India, Mali, the Democratic Republic of Congo, and Brazil, demonstrating its feasibility and potential to reduce geographic health inequities. However, implementing teleradiology in these settings faces several challenges, such as technological limitations, workforce shortages, regulatory and legal barriers, data security concerns, and cultural acceptance issues. Solutions and recommendations include investments in infrastructure, workforce training, supportive regulatory frameworks, secure data management, and adaptable telehealth models that consider socioeconomic factors in rural areas. By addressing these challenges, stakeholders can enhance the impact of teleradiology in rural healthcare systems, ultimately contributing to reduced disparities and improved healthcare outcomes. This review provides a comprehensive overview of the current landscape and suggests actionable pathways for advancing teleradiology in rural settings worldwide.

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Marketing Strategies in the Digital Age: Opportunities and Challenges

Purpose:Transformation and Response to Enterprise Marketing Strategies in the Digital AgeWith the rapid development of information technology, the digital era has come in all its aspects, bringing a new landscape to enterprise marketing strategy. The digital age brings many opportunities. First, there is more accurate market positioning, and enterprises use technologies such as big data, Can in-depth analysis of a variety of consumer data, accurate grasp of their needs and preferences, so as to clearly identify the target market, like millet through the analysis of user data, accurate positioning of young, the pursuit of cost-effective consumer groups, to launch products that meet their needs. Secondly, there is a wider distribution channel. The popularization of the Internet and social media has allowed enterprises to quickly spread their marketing information to all parts of the world, and Bytedance's products have utilized the advantages of their own platforms to achieve widespread information distribution. Moreover, more efficient customer interaction, enterprises can communicate with customers in real time through the online platform, timely access to feedback, improve customer satisfaction, such as Huawei through the online community interaction with users to improve products and services. However, the challenges cannot be ignored. Data security and privacy protection have become critical issues, and once a data breach occurs, it can seriously damage the reputation of a company, as some high-profile companies have been questioned over data security. The difficulty of accurate marketing is that the market changes rapidly, consumer demand is diverse and volatile, and it is difficult for enterprises to accurately grasp it at all times. The complexity of multichannel integration also causes problems for enterprises, and the characteristics and user groups of different channels are different, making integration difficult. Domestic and foreign enterprises have many successful experiences to learn from. For example, Apple pays attention to the user experience and has built a unique brand image and marketing model through the convergence of online and offline channels. In response to the challenge, enterprises should strengthen data security management and establish strict protection mechanisms. Continuous improvement of data analysis capabilities to better adapt to market changes; At the same time, we should focus on coordination between channels to achieve complementarity of strengths. In short, enterprises need to take full advantage of opportunities in the digital age, actively cope with challenges, and formulate effective marketing strategies in order to gain a foothold and develop in the fierce market competition.

BCAS: Blockchain-based secure access and sharing scheme for EHR data

Sharing Electronic Health Record (EHR) data is critical for improving medical decision-making and emergency care. However, EHRs contain sensitive patient information, making data breaches and misuse a significant concern during sharing. To tackle this challenge, we propose the Blockchain-Based Secure Access and Sharing (BCAS) scheme, a patient-centered, secure, and efficient solution for large-scale EHR access and sharing that intelligently combines blockchain with proxy re-encryption. Four smart contracts are designed for the BCAS to achieve effective user identity registration, precise virtual identity verification, strict access control, and secure data management. The adoption of hybrid on-chain and off-chain storage structures, coupled with the four smart contracts, effectively optimizes the efficiency of the blockchain in BCAS. Proxy re-encryption further enhances data confidentiality and streamlines EHR sharing among authorized users. The security analysis indicates that BCAS can resist DDoS, spoofing, and man-in-the-middle attacks, ensuring the privacy and security of patient EHR data. The performance evaluation demonstrates that the CPU usage of smart contracts does not exceed 15%. The on-chain and off-chain storage structures require less than 30s for uploading and downloading an 800MB file. Additionally, the proxy re-encryption scheme can process a 10MB file in less than 43ms. These results indicate that BCAS offers efficient performance without compromising data security and privacy. Compared with existing blockchain-based EHR sharing systems, BCAS offers superior performance, a more comprehensive solution, and increased resistance to attacks.

A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence in fog computing

Recent advancements in cloud computing have heightened concerns about data control and privacy due to vulnerabilities in traditional encryption methods, which may not withstand internal attacks from cloud servers. To overcome these issues about the data privacy and control of transfer on cloud, a novel three-tier storage model incorporating fog computing method has been proposed. This framework leverages the advantages of cloud storage while enhancing data privacy. The approach uses the Hash-Solomon code algorithm to partition data into distinct segments, distributing a portion of it across local machines and fog servers, in addition to cloud storage. This distribution not only increases data privacy but also optimises storage efficiency. Computational intelligence plays a crucial role by calculating the optimal data distribution across cloud, fog, and local servers, ensuring balanced and secure data storage.•Experimental analysis of this mathematical mode has demonstrated a significant improvement in storage efficiency, with increases ranging from 30 % to 40 % as the volume of data blocks grows.•This innovative framework based on Hash Solomon code method effectively addresses privacy concerns while maintaining the benefits of cloud computing, offering a robust solution for secure and efficient data management.

The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines

This study investigates the transformative role of Electronic Data Capture (EDC) systems in clinical trials, emphasizing their impact on data integrity, compliance with regulatory standards, and the efficiency of trial processes. The purpose of the research was to critically analyze how EDC systems align with FDA and ICH/GCP guidelines, addressing the complexities of modern clinical trials. By conducting a detailed literature review, the study explores the benefits, challenges, and technological advancements associated with EDC systems. Key findings highlight the significant advantages of EDC systems in enhancing data accuracy, real-time monitoring, and facilitating regulatory compliance through secure data management and audit trails. The study also identifies several challenges, including high implementation costs, data privacy concerns, and integration issues with other digital trial technologies. Technological advancements, such as artificial intelligence, blockchain, and decentralized trial models, are explored as promising solutions to these limitations, offering new avenues for optimizing clinical trial management. The study concludes that EDC systems have revolutionized clinical trials, streamlining data collection and analysis while ensuring compliance with stringent regulatory requirements. It recommends continued investment in EDC technologies, enhanced training for trial staff, and the integration of cutting-edge innovations to ensure future clinical trials' success.

Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption

This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.