A novel Security Aware Sensitive Encrypted Storage approach to improve the encryption of big data

Abstract

The data security and privacy have become a critical issue that restricts many cloud applications. One of the major concerns about security and privacy is the fact that cloud operators have the opportunity to access sensitive data. This concern dramatically increases user anxieties and reduces the acceptability of cloud computing in many areas, such as the financial industry and government agencies. This paper focuses on this issue and proposes an intelligent approach to cryptography, which would make it impossible for cloud service operators to reach sensitive data directly. The suggested method divides the file with precision using an intelligent classification technique. An alternative approach is designed to determine whether data packets need splitting to shorten operating time and reduce storage space. Our experimental assessments of both safety and efficiency performance and experimental results show that our approach can effectively address major cloud hazards and that it requires an acceptable computing time using an intelligent machine learning classification technique. We have proposed a novel approach entitled as a model for Security Aware Sensitive Encrypted Storage (SA-SES). In this model, we used our proposed algorithms, including Convolution Neural Network with Logistic Regression (CNN-LR), Elliptic-curve Diffie–Hellman-Shifted Adaption Homomorphism Encryption (ECDH-SAHE) and Elliptic-curve Diffie–Hellman-Shifted Adaption Homomorphism Decryption (ECDH-SAHD) .