A Data Leakage Identification System Based on Truncated Skew Symetric Gaussian Mixture Model
S. Praveen Kumar1, Y Srinivas2, M. Vamsi Krishna3
1S.Praveen Kumar, Research Scholar, Centurion University of Technology and Management, Rajaseetapuram (Orissa), India.
2Dr. Y Srinivas, Department of Information Technology, GIT Gandhi Institute of Technology and Management, Visakhapatnam (A.P), India.
3Dr. M. Vamsi Krishna, Department of Computer Science and Engineering, Centurion University, Paralakhemundi (Orissa), India.
Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 111-113 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1809017519©BEIESP
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© The Authors. 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: Data transfer from source to destination has become more essential as the organizations working under a frame need to exchange the data for processing the information and solving the necessary tasks. This concept of data transfer has become a most tricky task with the advent of hackers, intruders and other guilt agents who try to steal the sensitive data for unethical means. The present article addresses the issue of identifying such data leakages and also provides a platform for data preventing from such issues
Keywords: Truncated skew Gaussian Mixture, Hackers, Intruders, Guilt agent, Data Leakage.
Scope of the Article: Big Data Quality Validation