Enhance Data Security Research Articles

Secure Cloud Services for the Healthcare Industry: Addressing Unique Challenges and Ensuring Compliance

Abstract: The healthcare industry is increasingly adopting cloud computing to manage patient data, provide telehealth services, and support remote work. However, this adoption presents unique challenges due to stringent regulations governing the handling of personal health information (PHI), such as HIPAA in the United States and GDPR in the European Union. Noncompliance with these regulations can result in significant financial penalties, reputational damage, and loss of patient trust. This article explores the specific data security needs of the healthcare industry, the challenges in adopting cloud services, and the essential features of secure cloud services tailored for healthcare. It also discusses the integration of artificial intelligence (AI) and automation in cloud services to enhance data security and operational efficiency, as well as future trends in cloud computing for healthcare, such as the increasing adoption of AI and machine learning for predictive analytics and automated threat detection. By providing a comprehensive overview of the data security needs, challenges, and solutions in adopting cloud services for healthcare, this article aims to assist healthcare organizations in making informed decisions when considering the adoption of cloud services and to highlight the importance of prioritizing data security and compliance in the digital transformation of the healthcare industry.

Blockchain Targets Integrated IoT for Smart Healthcare Systems - A Bibliometric Analysis

Blockchain technology and the Internet of Things (IoT) for smart healthcare systems has emerged as a promising area of research, as evidenced by a bibliometric analysis. This analysis explores the scholarly literature on the integration of blockchain and IoT in healthcare, identifying key trends, influential publications, and research directions. By leveraging blockchain's decentralized and immutable nature alongside the IoT's ability to collect and transmit real-time health data from various devices, smart healthcare systems aim to enhance data security, interoperability, and patient privacy. Bibliometric analysis provides valuable insights into the evolution of research in this field, highlighting emerging topics, interdisciplinary collaborations, and potential applications that can revolutionize healthcare delivery and management in the future. This paper introduces the Weighted Key Signature for Smart Healthcare Systems (WKSS), a framework designed to optimize healthcare operations within smart environments. With advanced technologies such as blockchain, Internet of Things (IoT), and integration technologies, WKSS aims to improve patient care, resource utilization, and overall healthcare system efficiency. The framework encompasses key components including digital signature generation, computational efficiency, and bibliometric analysis to provide insights into emerging trends and key contributors within smart healthcare systems. Through a comprehensive analysis, including simulations and bibliometric studies, this paper demonstrates the effectiveness of WKSS in reducing patient waiting times, enhancing resource utilization rates, and improving healthcare provider efficiency. Through simulations and bibliometric analysis, significant improvements were observed in key metrics. The average patient waiting time decreased from 45 to 34 minutes, resource utilization rates increased from 70% to 85%, and examination room occupancy rose from 60% to 75%. Additionally, physician and nurse utilization rates improved from 80% to 90% and from 75% to 85%, respectively.

Orchestration constraints BlockChained smart contract and integrated digital twins manufacturing systems: ManuChain4.0-based application paradigm and case study

Decentralized manufacturing systems constrained by blockchain significantly enhances data security and process integrity. Nevertheless, intelligent workshops encounter hurdles related to scalability of nodes, efficiency in validation, configuration of process security, and management of online/offline operations, which pose challenges to the seamless application of blockchain in workshop manufacturing environments. This paper introduces an architecture based on BPMN (Business Process Modeling Notation) for integrating smart contracts and digital twins manufacturing systems. It explores effective strategies to address the challenges mentioned above, with the aim of enhancing integration and application approaches. (1) Utilizing a graphical model-driven solution for the visualization and configuration of manufacturing resources on the front end, creating a BPMN-driven orchestration diagram and an off-chain/on-chain interoperability model for manufacturing smart contracts, and investigating voting mechanisms and customization methods specific to smart contracts in workshop manufacturing; (2) The two-layer monitoring model based on blockchain 3.0 and Tangle-based verification backend is established by introducing Cached DTs to implement negotiation of smart contracts based on BPMN constraints at the upper level, and supporting device node expansion and verification mechanisms at the lower level, thereby realizing swift synchronization and comprehensive visualization across the entire manufacturing lifecycle; (3) Based on multiple open-source technologies, a decentralized intelligent workshop case that relies on distributed information management is constructed. The comprehensive experimental results underscore the technical superiority of the proposed system architecture.

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.

Application Status and Challenge of PROFIBUS Technology in Industrial Automation Field

This article first introduces the basic concepts and principles of PROFIBUS technology, and then elaborates on its application status in the field of industrial automation from three aspects: manufacturing, process control, and drive technology, including its wide range of applications and advantages. Subsequently, the main challenges currently faced by PROFIBUS technology were analyzed, such as integration with emerging industrial Ethernet technologies, data security issues, and the complexity of system upgrades and maintenance. Finally, the article looks forward to the future development trends of PROFIBUS technology, including further integration with industrial Ethernet technology and the introduction of encryption algorithms to enhance data security. Overall, PROFIBUS is a mature fieldbus technology. Still crucial in the field of industrial automation. However, with the development of new technologies and changes in application requirements, its future development still faces many challenges and opportunities.

Taxonomy of the GDPR-based Privacy Research by Scientometric Analysis

Will General Data Protection Regulation (GDPR) be adopted globally in business? The GDPR was approved in the European Union (EU) in April 2016 and officially put into effect in May 2018, thus the research in this field has an obvious upward trend. The development of GDPR is aimed at adapting to new trends, conducting scientific econometric analysis in the fields of privacy and GDPR, and analyzing and visualizing emerging trends. First, summarizing the privacy and GDPR studies publicly published between 1995 and 2023 through statistical analysis of terminology categories and high-yield journals. Then, understand the overall research status of privacy rights and GDPR from the perspectives of author, journal, literature co citation analysis, and collaborative networks. Finally, based on keyword analysis and literature co citation cluster analysis, a knowledge graph was constructed that includes knowledge domains, evolutionary trends, and future research directions. As a globally influential regulation, GDPR emphasizes the protection and lawful processing of personal data, which is of great significance for protecting personal data privacy and enhancing data security.

A copula-based secured intelligent dynamic-static energy community transportation system for smart cities

This paper addresses a secured co-dynamic model for the energy management of Electrical Vehicles (EVs) within the real community transportation system (RCTS). The proposed model aims to facilitate interoperability among mobile energy resources within the smart city, enabling the RCTS to model the co-dynamic-static transportation systems (TSs) simultaneously. The energy management model within the traffic flow system focuses on dynamic assignment, considering the power consumption associated with the density of moving vehicles. EVs play a key role in economically managing energy in both static and dynamic behaviors within charging stations while aligning with the current traffic flow.To enhance data security within the smart city ecosystem, a directed acyclic graph (DAG)-based decentralized cyber security approach is recommended. This approach ensures that data transactions involving mobile energy resources are secured against cyber-attacks through the use of public, private, and transaction blocks. Additionally, an uncertainty-based copula function is presented to create a precise management environment within the smart city. The results indicate that the proposed model for transportation energy resources tends to reduce energy costs by optimally controlling energy consumption within traffic flow, compared to normal conditions.

A quantum-secure certificateless aggregate signature protocol for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have revolutionized communication between vehicles and infrastructure, notably enhancing traffic management and passenger safety. However, VANETs are vulnerable to security threats, especially regarding data authenticity. Aggregate signature is a powerful technique that reduces computational and communication burdens by aggregating multiple signatures from different signers into a single signature. Traditional aggregate signature schemes, based on large prime number decomposition and the discrete logarithm problem, cannot effectively resist quantum attacks. This paper introduces a novel quantum secure certificateless aggregate signature (QSCLAS) scheme designed to enhance data security and privacy in VANETs. Our proposed scheme employs the number theory research unit (NTRU) algorithm. As a lattice-based cryptographic algorithm, NTRU is renowned for its security against quantum computer attacks, making it an essential component of our quantum-secure solution. By eliminating the need for expensive bilinear pairing operations, our proposed scheme achieves high efficiency and practicality in resource-limited VANETs environments. The security analysis demonstrates our scheme's resilience against both Type-I and Type-II adversaries in the random oracle model under the small integer solution (SIS) problem on the NTRU lattice. Furthermore, compared with existing approaches, the results illustrate that our proposed scheme offers significant advantages in signature generation and verification cost, as well as lower transmission overhead than other lattice-based schemes, thereby making it highly suitable for VANETs environments.

Revolutionizing healthcare with fog computing, IoT, and machine learning: An innovative framework for enhanced data security and QoS optimization

Revolutionizing healthcare with fog computing, IoT, and machine learning: An innovative framework for enhanced data security and QoS optimization

Enhancing quality-driven iot operations for smart cities: A comprehensive approach for routing, security, and optimization

This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods.

Improving Healthcare Data Security and Privacy With Blockchain technology using SHA-256 Algorithm

Block chain technology has the potential to completely transform healthcare management systems by enhancing data security, interoperability, and integrity because to its decentralized and unchangeable structure. Sensitive patient information is more difficult for bad actors to breach thanks to the SHA-256 algorithm, which improves data security. Interoperability between diverse healthcare systems is facilitated by smart contracts and standardized data formats. However, issues with user adoption, scalability, and regulatory compliance also face block chain implementation in the healthcare industry. This study offers important insights into the changing environment of healthcare data management by highlighting the possible advantages and challenges of integrating block chain technology in healthcare management systems.

User-Centric Cloud Framework for Enhanced Data Security by Applying UK Algorithm

Cloud storage is the technical boon which has become the realm of many companies like Amazon, Google, Microsoft, Drop box and many more. Remarkably all these companies are US based and thus eventually the personal data of every other nation is stacked on the Western companies. This leads the dependant country to a state of vulnerable. However if the dependant countries need to get autonomy they had to be provided with their own cloud storage which is impossible with the current mechanisms. With the facility of high computational devices and wide infrastructure and with the aid of secured encryption the cloud storage providers strongly protect the data of the users and enable their best service to the users. Also the cloud storage providers use the encryption algorithm in the provider infrastructure and data from user to provider infrastructure is done using the secured transmission. Finally the powerful infrastructure does the work to secure the data. This mechanism is certainly possible in the developed countries. But it is relatively impossible in the developing countries like India as it cannot afford the huge infrastructure to protect the public data. Even done, it might not result in generating profit because the subscription rate is comparatively lower in India than in developed countries. So to pursue the less profitable module is not suggestive and hence the reliability on western country cloud storage is over whelming. However to overcome this set back and to implement indigenous cloud storage this paper has attempted to implement the UK algorithm in user device environment through which mini and micro level cloud storage shall be developed resulting in the minimal cost of maintaining the cloud storage. In this case, cloud Storage provider do not need to have either the huge infrastructure nor the computational devices to process the data and no need of separate data transmission to secure the data as well. Employing the encryption algorithm in user environment helps to give full authority to the user on their data. The CS provider holds only encrypted data of the user and with low cost consumption this method is feasible even for the developing country like India.

Redefining governance: a critical analysis of sustainability transformation in e-governance.

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Optimization and Application of Cloud Storage Solutions in Remote Healthcare Data Management in India

Optimization and Application of Cloud Storage Solutions in Remote Healthcare Data Management in India

A novel deep learning technique with cryptographic transformation for enhancing data security in cloud environments

A novel deep learning technique with cryptographic transformation for enhancing data security in cloud environments

Federated Learning for Bronchus Cancer Detection Using Tiny Machine Learning Edge Devices

In deep learning, acquiring sufficient data is crucial for making informed decisions. However, due to concerns regarding security and privacy, obtaining enough data for training models in the era of deep learning is challenging. There is a growing need for machine learning (ML) solutions that can derive accurate conclusions from small data while preserving privacy. Smartphones, which are widely used and generate large amounts of data, can serve as an excellent source for data generation. One suitable approach for regularly evaluating real-world data from edge devices is Tiny Machine Learning (TinyML). With the increasing number of edge devices involved in transmitting private data, it's vital to have a method that allows computations to be performed on edge devices and pushed to the edge rather than over the network. Considering these obstacles, the combination of TinyML edge devices and Federated Learning can be applied in the early treatment of Bronchus Cancer. Under the framework of federated learning, local edge devices are trained independently and then integrated into the server without exchanging edge device data. This approach enables the creation of secure models without sharing information, resulting in a highly efficient solution with enhanced data security and accessibility. This article provides a comprehensive discussion of the key challenges addressed in recent literature, accompanied by an extensive examination of relevant studies. Additionally, a novel model based on edge devices and federated learning is proposed.

A Blockchain Based Security system framework in Healthcare Domain using IoT

In the contemporary healthcare domain, the integration of Internet of Things (IoT) technologies has revolutionized patient care, enabling real-time monitoring, enhanced diagnostics, and personalized treatment plans. However, this digital transformation is accompanied by unprecedented challenges in ensuring the security and privacy of sensitive healthcare data. Traditional cybersecurity measures often fall short in addressing the complexity and dynamism of IoT ecosystems. This research introduces a novel blockchain-based security system framework specifically designed for the healthcare sector utilizing IoT. By leveraging the inherent security, transparency, and immutability features of blockchain technology, the proposed framework provides a robust solution to safeguard healthcare data integrity, ensure privacy, and enable secure patient data sharing among authorized entities. Through a comprehensive system architecture, this paper delineates the integration of IoT devices with a blockchain network, highlighting the deployment of smart contracts for automated access control, and data encryption mechanisms to preserve confidentiality. An empirical evaluation demonstrates the framework's effectiveness in enhancing data security while maintaining system efficiency. The discussion extends to the implications of this framework for the future of healthcare IT security, addressing potential challenges and suggesting avenues for further research. This study not only contributes to advancing the state of cybersecurity in healthcare IoT but also lays the groundwork for future innovations in the field.

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

The digital world is rapidly coming together as well as transforming a lot of our valued data onto digital forms. Its consequent dissemination eased via the advent of the Internet has also encountered many attacks due to predictable responses from many users – leading up to social engineering and exploits on trust-level of users. The use of deception is now playing a very prominent role in enhancing data security. Several approaches abound to discourage and redirect challenges (via the use of honeypot), and to detect such intrusive activities (via an intrusion detection systems). These have been successfully used to minimize security breaches. We explore a deep learning deception-based honeypot to minimize breaches by adversaries. Used on web servers, it is equipped with identification capabilities as the system learns and defends a user system against intrusive actions. Our confusion matrix shows model has sensitivity of 0.81, specificity 0.08, and prediction accuracy of 0.991 with an improvement rate of 0.39 for data that were not originally used to train. . Keywords: Web Server, HoneyPot, Integratio, Injection Approach, Malware Intrusion Detection CISDI Journal Reference Format Malasowe B., Aghware, F. & Edim, B.E. (2024): Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 1, Pp 13-.28. dx.doi.org/10.22624/AIMS/CISDI/V15N1P2. Available online at www.isteams.net/cisdijournal

Transforming university records management: A comprehensive review of blockchain and self-sovereign identity applications

Traditional university records management systems often rely on centralized, fragmented, and vulnerable methods of data storage. This can compromise security, hinder efficient sharing of information, and limit students' ownership of their educational journeys. Blockchain technology, coupled with the principles of self-sovereign identity (SSI), offers the potential to revolutionize how student records are created, stored, and shared. This review paper synthesizes emerging research and explores the transformative implications of blockchain and SSI for university records management. It examines how these technologies streamline administrative processes, enhance data security and trustworthiness, empower students to control their narratives and foster collaboration between educational institutions. Key benefits, technical considerations, and real-world implementation challenges are critically analyzed. The paper concludes with a call to action for universities, technology providers, researchers, and policymakers to collaborate on innovative solutions. This collaboration will pave the way for a more secure, student-centric, and efficient future for educational records management.

Integrating Artificial Intelligence for Enhanced Data Security and Privacy

Integrating Artificial Intelligence for Enhanced Data Security and Privacy