Cloud data security with deep maxout assisted data sanitization and restoration process

Abstract

The potential of cloud computing, an emerging concept to minimize the costs associated with computing has recently drawn the interest of a number of researchers. The fast advancements in cloud computing techniques led to the amazing arrival of cloud services. But data security is a challenging issue for modern civilization. The main issues with cloud computing are cloud security as well as effective cloud distribution over the network. Increasing the privacy of data with encryption methods is the greatest approach, which has highly progressed in recent times. In this aspect, sanitization is also the process of confidentiality of data. The goal of this work is to present a deep learning-assisted data sanitization procedure for data security. The proposed data sanitization process involves the following steps: data preprocessing, optimal key generation, deep learning-assisted key fine-tuning, and Kronecker product. Here, the data preprocessing considers original data as well as the extracted statistical feature. Key generation is the subsequent process, for which, a self-adaptive Namib beetle optimization (SANBO) algorithm is developed in this research. Among the generated keys, appropriate keys are fine-tuned by the improved Deep Maxout classifier. Then, the Kronecker product is done in the sanitization process. Reversing the sanitization procedure will yield the original data during the data restoration phase. The study part notes that the suggested data sanitization technique guarantees cloud data security against malign attacks. Also, the analysis of proposed work in terms of restoration effectiveness and key sensitivity analysis is also done.

Keywords

Adopted data sanitization / Cloud data security / Restoration / Improved deep maxout / Optimal key generation

CRediT authorship contribution statement

Shrikant D. Dhamdhere: Writing - original draft, Conceptualization, Methodology. M. Sivakkumar: Validation, Data curation, Investigation. V. Subramanian: Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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