Bio inspired based Multi-objective Data-Privacy-Preservation-Technique (MO-DPPT) system model for cloud data

Abstract

The significance of data security in the cloud system has boosted the increasing quantity of sensitive and personal data being harvested by data controllers. As the cloud has more outsourced, unsecured sensitive data for public access, the data security for the cloud sectors is very important. The majority of state-of-the-art strategies fail to manage optimal privacy in implementing data privacy preservation model. To ensure data privacy preservation, most of the traditional techniques can perform the transformation on the actual data. These traditional methods are utilized limitedly, as they are pretty memory intensive and complicated. Hence, to defeat this dispute, this paper tries to develop a novel approach named Multi-Objective Data-Privacy-Preservation-Technique (MO-DPPT) that can look after the data privacy issues. The two main phases of the data privacy preservation model consist of the data sanitization and restoration process. Here, the optimal key generation is based on the proposed sanitization process and it is done by a novel meta-heuristic algorithm namely the Muddy Soil Fish Optimization Algorithm (MSFOA). The optimal key generation is achieved by developing the multi-objective function that focuses on the parameters of hiding ratio (HR) rate, Preservation Ratio (PR) rate, False Rule (FR) generation rate, and Degree of Modification (DM). Moreover, the proposed MSFOA algorithm has confirmed improved execution through statistical analysis, analysis on KPA and CPA attacks, and computational time analysis over the conventional algorithms. Finally, the proposed MSFOA based MO-DPPT system model using is compared with the existing traditional algorithms namely PSO, GWO, JA, SSO, JA-SSO, and the performance analysis of the proposed MSFOA based MO-DPPT system model has proven the superior efficiency in improving cloud security.