A Data Obfuscation Method Using Ant-Lion-Rider Optimization for Privacy Preservation in the Cloud

In this paper, an obfuscation-based technique, namely AROA-based BMCG method, is developed for secure data transmission in cloud. Initially, the input data with the mixed attributes is provided to the privacy preservation process directly, where the data matrix and bilinear map coefficient generation co-efficient is multiplied through Hilbert space-based tensor product. Here, bilinear map coefficient is the new coefficient proposed to multiply with original data matrix, and the OB-MECC encryption is utilized in the privacy preservation phase to maintain the security of the data. The derivation of bilinear map coefficient is used to handle both the utility and the sensitive information. The new algorithm called AROA is developed by integrating the ALO with ROA. The performance and the comparative analysis of the proposed AROA-based BMCG method is done using the metrics, such as accuracy and information loss. The proposed AROA-based BMCG method obtained a maximal accuracy of 94% and minimal information loss of 6%, respectively.