A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

In this paper, we plan a combined clustering and geometric data perturbation approach for improving privacy preservation of health care data in hybrid clouds. We will possibly plan an answer that can productively give protection to information put away in the cloud without presenting substantial overhead on both computation and communication. At first, the high-dimensional data are separated into various parts by utilizing the K-mean clustering method, each partition is considered as a cluster. At that point the mean estimate of each cluster processed; after that the contrast between the each cluster member and the mean of the cluster value is computed. In the next stage, the clustered information is perturbed by utilizing the Geometric Data Perturbation (GDP) algorithm which makes the values difficult to be recognized. These perturbed values are stored in the public cloud and the key parameters for randomizing and clustering is stored in the private cloud. Our approach would contribute in reduction of too much storage on private cloud on the off chance that we basically store the entire sensitive information on private clouds. The experimental results show that, the GDP algorithm has better privacy preserving compared with the other existing methods.