Privacy Issues in Big Data and Enhanced Privacy Preservation Model
Panakal Harly Mary1, Prajwal Sunil Solanki2, M Lakshmi3

1Panakal Harly Mary*, Department of Computer Science Engineering S.R.M. Institute of Science and Technology, Chennai, India 
2Prajwal Sunil Solanki, Department of Computer Science Engineering S.R.M. Institute of Science and Technology, Chennai, India
3Dr. M Lakshmi, Professor Department of Computer Science and Engineering S.R.M. Institute of Science and Technology, Chennai, India
Manuscript received on May 05, 2021. | Revised Manuscript received on May 08, 2021. | Manuscript published on May 10, 2021. | PP: 36-41 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1607051121
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Privacy is an essential factor for humans, but Big Data can expose set patterns from which trends specific to human behavior called personally identifiable information can be easily derived. We try to overcome the privacy issues in Big Data using traditional privacy preservation techniques, and Kanonymity is the most extensively used technique for preserving privacy for data publishing. This paper has investigated the privacy challenges in big data and proposes an enhanced privacy preservation model that protects the data against homogeneity and background knowledge attacks and maintains a balance between data quality and data privacy. The proposed algorithm gets executed with minimum running time. This technique will also aid data mining as it ensures data quality by reducing information loss.
Keywords: Big data, Data privacy, Data anonymization, K Anonymization, Information loss