Cluster-based convolution process on big data in privacy preserving data mining

The main intension of this paper is to privacy preserving-aware over big data in clouds using KNN and MapReduce framework. This paper consists of three phases such as, MapReduce phase, clustering the map reduced data and evaluation phase. In MapReduce phase, we are splitting the input data after the splitting process we are including a k-means clustering algorithm to cluster the map reduced data. Then, we are performing a convolution process to the dataset and create a new matrix. Once it is over, the privacy-persevering framework over big data in cloud systems is performed based on the evaluation base. In evaluation module, deduplication is performed with the aid of the KNN algorithm. In this phase using the KNN technique to check the duplication of data based on the threshold. Thus the non-duplicated data's are stored in cloud database, which is improving the utility of the privacy data.