A combined framework for data classification on data confidentiality levels for hybrid cloud storage using hybrid chaotic maps

Abstract

Cloud storage plays a pivotal role in modern data management yet requires robust security solutions to defend against potential vulnerabilities. Researchers behind this study focus on solving encryption challenges in hybrid cloud storage through adaptable encryption techniques due to the need for customized protection of various data confidentiality levels. The research introduces an integrated framework that uses hybrid chaotic maps to encrypt data according to a classification system designed for hybrid cloud storage. The system establishes different confidentiality ratings of Low, medium, and high by analyzing entropy as well as access frequency data. Security and efficiency are achieved through the implementation of three chaotic maps, including Logistic and Tent and Hybrid Map, which produce encryption keys for different levels. The proposed framework demonstrates superior performance according to an analysis that compares it with Blowfish and ECC algorithms for encryption/decryption times as well as entropy and energy use and throughput. The Python-based framework delivers efficient and secure encryption services while maintaining strong randomness capabilities, which make it appropriate for hybrid cloud scalability needs.