CDFM-based Secure & Efficient Architecture for Data Management in Cloud Computing

Abstract

Cloud has become a prominent technology of data storage and computation. This allows user and institutions to depend on Cloud providers for storage and computing to offer system as a service. The growing significance of extensive acquisition and adoption of cloud computing have imposed security assurance on cloud. Security assurance provides integrity, confidentiality and reliability of data by performing computation and retrieving in compliance with the cloud providers. The storage, processing and managing of data for complex application becomes cumbersome and burdensome on cloud provider platforms. A management mechanism is required to manage the resources and reduce the complexity for maximizing performance with minimum human intervention. Even handling security threats occurring with varying workloads and failures in cloud environment is entailed. Motivated from this self management mechanism, we proposed a novel Cuckoo-based Data Fragmentation and Metadata (CDFM) secure and efficient approach. It manages the complex data of Translation and retrieves the translated data securely. In this work, cuckoo pools divide the data into different number of fragments and send it to different data pools. Data pools first encrypt the data fragment and assign color code to this encrypted fragment and then prepare indexing according to this color coding. The performance analysis, exhibit higher performance than existing approach for security and data fragmentation in cloud scenario.