Privacy Preserving in Big Data using Cloud Virtulaization

The large amount of sensor data leads to complexity of Big Data, which contains both necessary as well as unnecessary information. In order to get the necessary information from this big data, there is a need for classification and prediction techniques. Here the classification is done by two different algorithms namely, C4.5 and proposed work contains combination of C4.5 and SVM (Support Vector Machine). C4.5 is an algorithm used to generate a decision tree which is used for classification, and for this reason, it is often referred to as a statistical classifier. A Support Vector Machine (SVM) performs classification in the basis of multiclass classification, i.e., “one against many” where each category is split out and all of the other categories are merged. The performance of both classifiers is analyzed. The resultant shows proposed work performs better classification when compared to C4.5 classifier. The future predictions are also calculated and saved in the form of dataset. This dataset can be retrieved by server and partitioned as packets. Then, it is transferred to more than one client simultaneously by means of interfacing unit. Privacy preservation can also be achieved by encryption and decryption while the transfer of data takes place. This process reduces the overload of transferring entire data.