An improved privacy aware secure multi-cloud model with proliferate ElGamal encryption for big data storage

The isolation of sensitive attributes in the customer profile and the uploading of encrypted data to the multi-server-based cloud are the major issues in the existing applications. This paper proposes the novel rule-based statistical disclosure method (RSDM) and access control policy-based access restriction (ACPAR) to integrate the activities of sensitive attribute prediction and the data uploading stages in cloud computing. Initially, the normalisation based on the hide and visibility metric assignment to the fields in the dataset used to isolate the sensitive and normal attributes in the customer profile. Then, the data encryption is performed through proliferate ElGamal algorithm sequentially and stored into the cloud. The RSDM serves as the base for sensitive data isolation. Then, the access control policy is designed to control the profile-viewing ability to assure the security. The proposed work decrypts the data associated with the denormalised profile for integrity.