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Cloud computing (CC) is a network‐based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online‐based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small‐to‐medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.
Cloud computing (CC) is a network‐based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online‐based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small‐to‐medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.
Due to its heterogeneity, cloud services providers' (CSPs) rapid expansion presents several challenges, such as optimal service selection and privacy preservation. Multiple users using the cloud service at once increases the delay for service selection and request. Service interruptions result from centralized provisioning and insecurity. Existing work constraints include access control loss, service disruptions, security issues, trust management issues, and delays. Blockchain-based request scheduling and optimal CSP selection in edge-assisted clouds were presented in this research. Five phases-Data User (DS) authentication, sensitivityaware request scheduling, policy verification, trust management, and optimal CSP selection-are proposed. In the first phase, DU authentication detects and eliminates authorized users. We suggested a chaotic map-based camellia encryption algorithm (CMCE) to boost security. The gateway schedules service requests using Johnson's rule-based Stochastic Gradient Descent method, considering delay, throughput, and priority, in the second phase. This schedules the request into sensitive and non-sensitive services. Policy verification is done in the third phase utilizing Dynamic Policy-based Access control, which allows only sensitive requests. In phase four, we calculate the CSP trust value to boost security. Based on CSP behavior, we introduced the Multi Behavior Analysis-based Nomadic People Optimizer method. Every CSP's trust value is modified based on user feedback over time. Finally, the best CSP is chosen for data user service, and suggested Dynamic and non-cooperative Game Theory is to choose the best CSP from a list. CloudSim is used to simulate and assess.INDEX TERMS Blockchain, Cloud Computing, Edge, Scheduling
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