An enhancement to SePeCloud with improved security and efficient data management

Abstract

Outsourcing data to a third-party controlled cloud computing services arises various security issues. High security schemes are required to protect data in the cloud platform. Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) method addressed these issues by improving the security and performance of a cloud environment. An enhanced version of DROPS named SePeCloud was proposed that improved the security and performance hand-in-hand with Fog based Deduplication and Privacy Preserving Online Updating. This paper improves the security and data storage with Self-Destruction mechanism by handling the applications independently built on reinforcement learning strategy. Data chunk fingerprint index and sketch index are included to support independent and parallel data destruction among multiple applications. Additionally, SePeCloud is further extended to prevent impersonation attacks from illegitimate users by introducing Modified Shamir secrete sharing scheme to handle user revocation policies with limited storage space. The experimental results prove that the final version of the SePeCloud performed better than the previous versions in terms of Replication cost savings and Computation time by improving both security and performance of the cloud system.