Data Encryption Strategy with Privacy-Preserving for Big Data in Mobile Cloud using H2Hadoop

Abstract

Cloud Computing leverages Hadoop framework for process Big Data in parallel. Hadoop has bound limitations that could be exploited to execute the duty efficiently. These limitations square measure principally thanks to data section within the cluster jobs and tasks scheduling, and resource allocations in Hadoop. Economical resource allocation remains a challenge in Cloud Computing MapReduce platforms. We propose H2Hadoop that is an enhanced Hadoop design that reduces the computation value related to Big Data analysis. The projected design also addresses the difficulty of resource allocation in native Hadoop. H2Hadoop provides a better resolution for “text data”, like finding DNA sequence and the motif of a dna sequence. Also, H2Hadoop provides an efficient Data Mining approach for Cloud Computing environments. H2Hadoop architecture leverages on Name Node’s ability to assign jobs to the TaskTrakers (Data Nodes) inside the cluster. By adding control options to the Name Node, H2Hadoop will intelligently direct and assign tasks to the Data Nodes that contain the desired knowledge while not causing the duty to the full cluster. Comparing with native Hadoop, H2Hadoop reduces cpu time, range of read operations, and another Hadoop factors.