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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.

Modified Apriori Based Data Sanitization for Cloud Data Security: An Optimization Assisted Model