This paper proposes a novel data processing framework that integrates edge computing and blockchain technology for real-time streaming. The framework provides secure and scalable processing of large datasets in distributed networks, enabling real-time data analytics while ensuring data integrity and privacy.

Edward Samuel Johnson, Jessica Amelia Brown, Oliver George Williams, Elizabeth Ruth Lee, Alexander James Scott

This research presents a secure cloud computing architecture designed to protect data privacy in distributed machine learning systems. By incorporating encryption techniques and secure multi-party computation, the architecture ensures the confidentiality of sensitive data during model training and inference processes in a cloud environment.

Matthew William Thomas, George Robert Johnson, Emma Olivia Clark, Jacob Benjamin Harris, Lucy Catherine Scott

This paper explores the application of machine learning algorithms for detecting cyber threats in cloud-based healthcare systems. The proposed system leverages anomaly detection and classification techniques to identify potential security breaches, enhancing the resilience and privacy of sensitive health data in the cloud.

David John Taylor, Christopher Robert White, Michelle Anne Harris, Jason David Carter, Claire Louise King

This research investigates the use of distributed ledger technology (DLT) for secure data sharing in IoT-enabled cloud environments. The proposed approach enhances trust and accountability in data exchange between IoT devices and cloud platforms, ensuring data integrity and privacy while maintaining system scalability.

Jonathan Michael Adams, Sarah Elizabeth Miller, Andrew Thomas Robinson, Rachel Jessica Walker, Daniel Simon Evans

This paper presents an optimization model for resource allocation in edge computing systems, aimed at enhancing real-time data processing. The model dynamically adjusts resource distribution based on the current computational load, improving system performance and reducing latency for time-sensitive applications.

Lucas Robert Anderson, Thomas Mark Phillips, Laura Jane Clark, James Richard King, Charlotte Amelia Harris

This paper proposes a blockchain-based access control mechanism for cloud storage systems in smart cities. The mechanism ensures secure and transparent access to sensitive data stored in the cloud, leveraging blockchain's immutability and decentralization features to protect user privacy and prevent unauthorized access.

Oliver Daniel Miller, Jennifer Elizabeth Brown, Emily Catherine Smith, George Edward Turner, Samuel Michael Harris

This study presents an IoT-driven cloud computing model designed for real-time monitoring and control in industrial automation systems. By integrating IoT devices with cloud infrastructure, the model enables efficient data collection, real-time analysis, and decision-making to optimize manufacturing processes and enhance system performance.

Henry Charles Cooper, Sophia Rose Bennett, William Daniel Green, Alice Margaret Johnson, Jack Andrew Carter