Data Classification Using Sardine Optimized Adversarial Generative Quaternion Network with Fibonacci Q-Matrix Hyperchaotic Encryption Schemes in Cloud Kooragayala Sukeerthi, R. Kesavan, S.A. Kalaiselvan 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 In the era of cloud computing sensitive information management requires data classification to function securely across all industries. A protected framework for cloud-based data analytics with privacy preservation emerges through integrating sophisticated encryption techniques with neural networks. The need to protect data grows increasingly critical because organizations must specify precise performance measures as well as classification accuracy metrics. The developing thrilling cybersecurity and cloud data disclosure world continues to inspire many trailblazing privacy protection solutions that leap forward unimpeded. The research proposes and builds a new framework called AS-FiQ-AgQN, which fuses adaptive optimization with advanced encryption and secure classification. The framework secures data confidentiality by employing Fibonacci Q-Matrix Hyperchaotic Encryption. Data classification on the encrypted data is performed using Adversarial generative Quaternion Network which is further optimized using Adaptive Sardine Optimization for better performance of the network. Performances have proven to yield astonishing results, with accuracy above 99.5 %, precision of 99.3 %, and recall above 99.7 %, which affirms the efficiency with which the system operates. Through these evaluations researchers confirm the framework's success in both precise data categorization while ensuring encryption safety at the cloud level. Data classification based on AS-FiQ-AgQN produces a robust secure system to protect privacy that enables accurate predictive cloud analytics with reliable data protection.
Smart Health Prediction Using Random Forest Kooragayala Sukeerthi, Kurma Pooja Reddy, Shaik Thasleema, Paindla Sowjanya Springer Proceedings in Mathematics and Statistics, 2025
A HEART DISEASE DIAGNOSIS SYSTEM EMPLOYING RESIDUAL CONVOLUTIONAL NEURAL NETWORKS WITH ADAPTIVE CROSS-LAYER STACKED ARCHITECTURE Journal of Theoretical and Applied Information Technology, 2024
KNN-BASED SENSITIVITY CLASSIFICATION AND HOMOMORPHIC ENCRYPTION FOR CLOUD SECURITY AND PRIVACY Journal of Environmental Protection and Ecology, 2024
A Detailed Study on Security and Privacy Analysis and Mechanisms in Cloud Computing Kooragayala Sukeerthi, R. Kesavan, S. A. Kalaiselvan 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023 When it comes to delivering IT services to businesses or consumers, the cloud provides a scalable, reliable, and cost-effective option. However, there is an increased risk associated with cloud computing due to the fact that critical operations frequently go to another organization, making it more difficult to preserve privacy and security of data, sustain data and availability, and prove compliance. It is a difficult issue in cloud computing to ensure the security of outsourced data. Data integrity should be verified by the client or an external auditor before being outsourced. Many methods using various techniques, including Proof of Retrievability, proved data ownership, privacy preservation, etc., have been developed for this goal. Various security strategies to address the current security vulnerabilities have been offered in the literature by researchers and impacted companies. Security and privacy concerns in cloud computing are also well reviewed in the literature. Unfortunately, the literature presented works lack the adaptability to counter diverse attacks without undermining cloud security goals. As a corollary, the literature has highlighted security and privacy vulnerabilities without giving suitable technological techniques to alleviate both security and privacy risks. However, studies that provide technological responses to security issues have not provided enough reasoning for the existence of these dangers. The purpose of this article is to discuss potential cloud security and privacy challenges that might arise and need a flexible approach to finding a workable solution. This study provides a comprehensive review of the relevant literature, discussing how safe cloud conflicts have made the suggested models from prior works obsolete while also taking into consideration the works' adaptability to handle future threats. The STRIDE methodology is then used to convey, from the user's point of view, the security concerns associated with cloud computing. It also analyzes the literature's many ineffective solutions and gives guidance on how to set up a safe, flexible cloud infrastructure.
