Enhancement of Cloud Stationed Healthcare Information Security by Dimensionality Reduction
S. Gnana Sophia1, K. K Thanammal2

1S. Gnana Sophia M.Sc.,M.Phil., Research Scholar , S.T. Hindu College in Nagercoil, Tamil Nadu, India.
2Dr. K. K Thanammal, Associate Professor, Department of Computer Science and Applications S.T.Hindu College, Nagercoil, Tamil Nadu, India.
Manuscript received on August 15, 2019. | Revised Manuscript Received on August 20, 2019 | Manuscript published on August 20, 2019. | PP: 6-10 | Volume-5 Issue-5, August 2019. | Retrieval Number: D0921055419/2019©BEIESP | DOI: 10.35940/ijies.D0921.085519
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The security of healthcare information can be secured by the use of cloud environment, and takes finite estimating power. The security of patient’s data shared over the internet can be distressed by healthcare institutions because of growing high popularity. The Eigen decomposition (ED) and Single Value Decomposition (SVD) of a matrix are relevant to maintain the security and the study of Dimension Reduction and its advantages are also applicable. To reduce the data without loss, Principal Component Analysis (PCA) is used. Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed highdimensional features or complex distance functions into a lowdimensional space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernel based data The proposed method covers how to generalize locality-sensitive hashing and the implementation of Kernel PCA based methods for Dimensionality Reduction can be applied to Medical data provides high security and utilize the resources of the cloud to inhibit data efficiently.
Keywords: Principle Component Analysis(PCA), Kernel Principle Component Analysis (K-PCA), Single Value Decomposition (SVD), Eigen decomposition (ED)