A Research on Bigdata Privacy Preservation Methods
S. Subbalakshmi1, K. Madhavi2

1S. Subbalakshmi, Research Scholar, Department of CSE, JNTUCEA, Ananthapuramu (Andhra Pradesh), India.
2Dr. K. Madhavi, Associate Professor, Department of CSE, JNTUCEA, Ananthapuramu (Andhra Pradesh), India.
Manuscript received on 03 June 2019 | Revised Manuscript received on 28 June 2019 | Manuscript Published on 04 July 2019 | PP: 175-177 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10350681S419/2019©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: Big data is massive collection of big and complex data sets that cannot be stored and processed using traditional data processing systems. Hence big data requires high computational power and storage and big data uses distributed system. Big data Analytics means analyzing invisible data patterns from the larger data sets. The data sets are gathered from various sources i.e. social media, Business sector, healthcare, data governance, various institutions, etc. So, privacy and security is main concern in big data. This paper mainly focuses on l anonymity techniques preserve the privacy of data. This research aims to highlight three main Anonymization techniques used in a medical field namely, k-anonymity, l-diversity, and t-closeness.
Keywords: Big Data, Privacy, Security, Data Anonymization Anonymity, L-Diversity, T-Closeness.
Scope of the Article: Security, Trust and Privacy