Progression in cloud applications have been hindered due to critical issues such as Data security and privacy. The aggravating concern remains the ability and access of cloud operators in acquiring sensitive data. Consequently, usage and excitement of cloud computing has remained lukewarm, with users remaining skeptical of the level of confidentiality promised. Principally, the financial industry and governmental agencies have shown lackluster and inimical enthusiasm of these indications. The paper serves to analyze the aforementioned issue, and present a creative proposal into cryptography, as a means to virtually restrict cloud service operator’s access to sensitive data. The free-will of cloud operators in handling sensitive data will be undermined. Through this method, file is divided with more accuracy using an intelligent classification technique. Alternatively, a different method can be utilized to find out whether data need splitting to obtain lesser operating time and minimize storage space. The results show that the approach can resolve innumerable risks associated with cloud computing, whilst requiring sufficient computing time using a very good intelligent machine learning classification techniques. As such, novel approach has been proposed which is entitled as a model for Sensitive Encrypted Storage (SES). In this model, three proposed algorithms Convolution Neural Network with Logistic Regression, Elliptic-curve Diffie Hellman-Shifted Adaption Homomorphism Encryption and Decryption have been integrated.

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) .

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