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.

This study proposes a new approach to cloud data security, with two distinct processes: proposed data sanitization and restoration. By hiding it from unwanted users and access, the data sanitization procedure ensures the protection of sensitive data stored in massive databases. In this case, the Modified Apriori approach is used to clean the data. In this scenario, the central goal is to generate a key, which is done using an optimization algorithm. Recovery or retrieval of cleaned data is a reversible process in data recovery. The optimization key for both sanitization and restoration will be discovered using the proposed MFUWOA. The optimal key for both restorations as well as sanitization will be determined using the proposed MFUWOA. While resolving the optimization challenge, the targets stated include the data retention ratio, hidden ratio, and level of alteration. Moreover, the adopted MFUWOA model for dataset 1 attains the best fitness value (∼0.2) to extant schemes including WOA (∼0.3), MFO (∼0.7), PRO (∼0.34), SSO (∼0.8), CMBO (∼0.4), and ALO (∼1.0), correspondingly.