A Novel Approach for Privacy Preservation in Bigdata Using Data Perturbation in Nested Clustering in Apache Spark

Now a days with the emerging technologies the surplus amount of data and security features faces a major problem. In order to handle this problem there are many innovative research and applications are processing. It plays a vital role in todays technological world. This paper come up with a technique to handle the above problem in an adequate way. Apache Spark is a memory cluster computing platform, it is 10 to 100 times faster than map reduce in batch processing, sparks have a graph X, a distributed graph system. It supports machine learning algorithm for future prediction. There is many privacy preservation techniques are there. This paper is going to propose a technique ‘Data Perturbation in Nested Clustering’ (DPNC) for numerical and non-numerical data to enhance the privacy. The perturbated data will store in Hadoop through Apache Spark for third party access for research or survey purpose. In this method the data will be preserved and hasty processing of data.