access icon free Effective convolution method for privacy preserving in cloud over big data using map reduce framework

© The Institution of Engineering and Technology
Received 30/06/2018, Accepted 08/01/2019, Revised 21/11/2018, Published 22/03/2019

Cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sensitive information accumulated in the cloud. In this research, the authors have proposed a competent large data convert among privacy defending by Hadoop map reduce in the cloud. The procedure exploits fuzzy C-means clustering (FCM) algorithm grouping the data. For dimensionality reduction, map reduce framework will be used. In evaluation module, the recommended technique performed with the aid of K-nearest neighbour (KNN) classification algorithm in this phase using KNN technique to check the convolution process based on the threshold value, which is improving the utility of the privacy data. The consequence acquired illustrates that authors’ proposed scheme has enhanced the clustering exactness and also accomplishes the effectual convolution procedure to improve the privacy. From the experimental results, the proposed research achieved an effective clustering accuracy 76.07% and the existing K-means approach gets the clustering accuracy of 73.5% which is minimum value when compared to the proposed researches. The suggested technique is implemented in JAVA with Cloud Sim platform.

Inspec keywords: fuzzy set theory; pattern clustering; Big Data; security of data; pattern classification; parallel processing; nearest neighbour methods; cloud computing; data privacy

Other keywords: effective clustering accuracy; evaluation module; malevolent assault; remote servers; dimensionality reduction; convolution process; recommended technique; Cloud Sim platform; K-means approach; Hadoop map; Internet; KNN technique; privacy preserving; conventional privacy defending process; effectual convolution procedure; big data; FCM algorithm; sensitive information; cloud computing; map reduce framework; clustering exactness; effective convolution method; fuzzy C-means clustering algorithm; threshold value; K-nearest neighbour classification algorithm; privacy data; JAVA

Subjects: Parallel software; Other DBMS; Combinatorial mathematics; Data security; Internet software

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