Big-data driven approaches in materials science on cloud storage API through optimal homomorphic encryption

Abstract

Its size and complexity, cloud externalization is considered for enhancing security in API. Nonetheless, cloud storage API has two key problems that allows user usability to be effectively functioning and that securely update cipher text in cloud through a new access policy for data owner. This paper recommends an effective secure access update algorithm based on an optimum cryptographic algorithm for API in cloud. Proposed method uses an optimal homomorphic Encryption (OHE) algorithm for secure storage. With Adaptive Salp Swarm Optimizations (ASSO), key values for the OHE algorithm are optimally selected. The proposed project also allows cloud server to efficiently update cipher text and monitor updates against cloud misbehaviors when a new access policy has been mentioned by data owner. It enables data proprietor and skilled users to effectively monitor legitimacy user data access and allow the user to verify that information provided by others is correctly recovered in plaintext. For that, the suggested method utilizes the attribute based access control model. Its analysis reveals how the policy reform program is formulated accurately, completely, safely and efficiently. The performance of the method proposed is measured by differentiating data and the cluster size due to its memory and execution times. Unlike the existing systems with a secure update in the access policies, this approach consumes minimum memory and time. The proposed process is introduced in cloud handset to Hadoop Map Reduction Framework.