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
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.