SECURING MEDICAL IMAGES IN CLOUD COMPUTING ENVIRONMENTS

Cloud computing provides significant benefits such as scalability, cost efficiency, and flexibility in accessing computing resources. However, its adoption introduces critical security challenges for organizations, necessitating a thorough analysis of associated risks. This research identifies and categorizes the primary security threats in cloud computing, including data loss, data leakage, cyberattacks, and unauthorized access, and evaluates their impact on organizational operations, data security, and regulatory compliance, with a focus on the financial and healthcare sectors. The study reveals that cyberattacks are the most prevalent and disruptive threat, while data leakage leads to significant legal repercussions and loss of trust. Practical recommendations include adopting encryption, multi-factor authentication, and strict access controls, as well as complying with industry standards like PCI DSS for finance and HIPAA for healthcare. Additionally, a comprehensive risk management framework comprising risk identification, assessment, mitigation, and continuous monitoring is proposed to enhance organizational resilience. By implementing these strategies, organizations can securely leverage cloud computing, maintain regulatory compliance, protect sensitive data, and uphold customer trust

Cloud computing is the delivery of various services via the Internet. Data storage, databases, networking, software etc are examples of these resources. Cloud is growing daily and encounters numerous challenges. Among the challenges one of them is scheduling. There are many types of scheduling, one of them is task scheduling. In cloud computing, task scheduling is a issue that reduces system performance. The task scheduling issue is one of the critical issues in the cloud computing because cloud performance is heavily reliant on it. An effective algorithm is needed to increase system performance. In this paper, we attempt to study the performance of a task scheduling algorithm in a cloud data centre.

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.

The convergence of digital twin (DT) technology with artificial intelligence (AI) and cloud computing has given rise to powerful real-time systems capable of monitoring, simulating, and optimizing complex physical processes. However, this integration introduces a wide range of cybersecurity concerns. From adversarial AI threats to cloud infrastructure vulnerabilities, AI-powered digital twins operating in distributed cloud environments face both unprecedented opportunities and risks. This review explores the evolving landscape of digital twin security, emphasizing the dual role of AI as both a potential attack surface and a defensive mechanism. It analyses existing literature on threat models, AI-driven security frameworks, and conceptual architectures, ultimately identifying gaps and directions for securing next generation AI-integrated digital twins in the cloud.

Cloud computing environment (CCE) can empower an association to re-appropriate computing resources to increase monetary benefits. For both developers and the cloud users (CUs), CCE is transparent. Accordingly, it presents new difficulties when contrasted with precedent types of distributed computing. The precision of assessment results in CCE security risk assessment to take care of the issue of the multifaceted nature of the system and the classified fuzzy cloud method (CFCM) applied to CCE chance ID stage that captures the CCE risk factors through a complete investigation of CCE security area. Current CCE frameworks present a specific restriction on ensuring the client’s INFO privacy. We offer a homomorphic proxy re-encryption (HPRE) in this paper that enables various CU to share INFO that they redistributed HPRE encrypted utilizing their PubKs with the plausibility by a close procedure such as INFO remotely. The test of giving secrecy, uprightness, and access control (AC) of INFO facilitated on cloud stages is not provided for by conventional AC models. CFCM models were created through the duration of numerous decades to satisfy the association’s necessities, which accepted full authority over the physical structure of the assets. The hypothesis of the INFO proprietor, an INFO controller, and a supervisor is available in the equivalent trusted area. Besides, CCESR features like the essential unit, fuzzy set (FS) hypothesis, and EW strategy utilized to precisely measure the likelihood of CCE security risks (SR) and the subsequent damages of CCESR estimation. Eventually, the computation and authentication model specified, and the lack of CCE SECU threat evaluation examined.

In cloud computing, the challenge of countering hidden information security threats is becoming increasingly critical due to the high dynamism of resource management, the complexity of verifying interprocess interactions, and the widespread use of virtualized environments. Particular attention is paid to threats that emerge at the hypervisor level or result from uncontrolled transactions between guest operating systems and cloud control subsystems, which renders them undetectable by traditional monitoring tools. To address these challenges, an instrumental approach is proposed that implements mechanisms for detecting and neutralizing latent attacks through continuous monitoring of system resource requests and behavioral analysis of component interactions. A formalized model of information interaction has been developed within this study, representing the logic of sequential and parallel operations initiated by virtual machines when accessing computing, networking, and storage resources. This model enables not only the structuring of dynamic information flows but also the formalization of critical dependencies between transactions that could serve as vectors for hidden attacks. A threat identification method based on predicate logic is applied, taking into account the context of system call execution, including signs of anomalous activity and deviations from the active security policy. The results obtained confirm the practical feasibility of using formalized models of transactional interaction and predicate analysis to enhance the security of cloud services against complex and hidden information security threats. This is especially relevant in the context of the growing adoption of containerization, orchestration, and distributed computing technologies, particularly in environments such as AWS, Azure, Google Cloud Platform, and Kubernetes.

Cloud computing represents a significant challenge for securing data due to its inherently distributed model. Elliptic Curve Cryptography (ECC) will provide substantial security with smaller key sizes, creating a strong candidate for cloud-based environments. This review critically examines ECC based security architectures in cloud computing with the aim of reducing the cipher space for efficiency and scalability. A comprehensive examination of recent ECC implementations guided this review, including identity-based and lightweight ECC Architectures. Specific consideration was given to cipher space reduction techniques incorporating point compression and optimized scalar multiplication. The review provided evidence of a gap in the literature regarding the ability to counterbalance strength of security with the compactness of the cipher across a range of cloud scenarios and use cases. Most models considered provide little flexibility for real-time or post-quantum alternatives. None of the literature considered cipher space optimization for ECC to date. The ongoing optimization of cipher space is essential to progressing security based on ECC implementations for cloud scenarios. Future research efforts must centre on adaptive, efficient, quantum resilient ECC schemes, in ways that yield high security with minimum impacts on the operational scale, and the associated computational loads.

Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.

In China, with the continuous advance of cloud computing technologies such as virtualization technology and big data processing technology, small and medium-sized enterprises are constantly updating and improving their management information system. In recent years, computer network technology and information technology have been gradually applied to enterprise management. With the development of enterprises, a large number of heterogeneous software services deployed on different servers are accumulated in enterprises. Cloud computing can not only reduce the cost of informatization construction of small and medium-sized enterprises, strengthen the integration of information resources, promote resource sharing, but also promote enterprise management and service innovation. In view of the development of social economy and the advance of the industry, if the enterprise wants to be better based on the market, it must realize the scientific management mode, and constantly improve the efficiency and quality of work. Through the cloud computing model, this paper introduces the system construction of the management information system for small and medium-sized enterprises in the cloud computing environment and the implementation scheme of enterprises in the cloud computing environment.

The cloud computing is an Internet-based resource sharing system in which virtualized resources are provided over the Internet. Cloud computing refers to a class of systems and applications that employ distributed resources for use in various applications; these computing resources are utilized over a network to facilitate the execution of tasks. However, cloud computing resources are heterogeneous and dynamic, connecting a broad range of resources. Thus, there are a large numbers of application and data center in the cloud computing environment. Therefore, the security issues of authentication and communication in application services and data center need to be considered in the cloud computing environment.

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Virtualization is very important factor to reduce cost of data warehouse deployment. It can reduce cost as well as tremendous pressure of managing devices, storage servers, application models and man power. In current era, data warehouse is very effective concept that can make huge impact in organizational decision support system. Unlike database systems, data warehouse systems take large amount of time and cost to deploy and develop in-house system for an organization. This is the reason that people now think about cloud computing as a solution of the problem instead of implementing their own data warehouse system. This paper is about, how cloud computing environment can be established as an alternative of data warehouse system. It will also throw some light about better environment choice for the organizational need. Organizational data warehouse and EC2 (Elastic Cloud Computing) are discussed with different parameters like ROI, security, scalability, robustness of data, maintenance of system etc. Paper will help in comparative analysis of both the systems.

Medical images contain sensitive and private health information of patients, which is crucial to be safeguarded from unauthorized access. Various encryption schemes are used for improving security; however, they are subject to adversaries associated with higher complexity, ineffective compression ratios, and slower responses in real time. Therefore, to address these limitations as well as to establish stronger security in medical image sharing, the research proposes a Biometric Fused Federated Generative Adversarial Network (BF2GAN) method. The federated learning (FL) concept is included in this research, which provides collaborative training while reducing the risks of gradient inversions. Moreover, the decentralized training strengthens data integrity and reduces the risk of data exposures. The biometric information is included for user verification and key generation, which assists in creating highly secure images that are difficult to decode without the correct keys. The method minimizes the reliance on key management algorithms and establishes a simplified encryption process that strengthens the overall system security. In the LUNA16 database, the BF2GAN achieves a significant performance in terms of 3.08s encryption time, 0.975 structural similarity index measure, 0.82 Feature similarity index, 3.9s decryption time, 58.38 decibels of peak signal-to-noise ratio, and minimum memory usage of 280.87 kilobytes, compared to the state-of-the-art methods.

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

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.