Mapreduce: Simplified Data Processing on Clusters with Privacy Preserving By using Anonymization Techniques
Ashutosh Dixit1, Nidhi Tyagi2

1Ashutosh Dixit, Research Scholar, Bhagwant University, Ajmer, Rajasthan, India.
2Nidhi Tyagi, Professor, MIET, Meerut.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3233-3239 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7773038620/2020©BEIESP | DOI: 10.35940/ijrte.F7773.038620

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Computerized Data from various sources, such as remote sensors, cutting-edge sequencing of bioinformatics and high-performance instruments, are increasing at extremely high speeds. To keep analyzing through results for programming, facilities and measurements, The Researches have to use new procedures and techniques. Google’s team started MapReduce programming system which aims to manipulate huge data sets in disseminated frameworks; this design lets software engineers create applications that are extremely valuable to large data processing. The motive of this paper is to explore MapReduce research techniques and to increase current research efforts to improve the execution of MapReduce and its capabilities.
Keywords: Anonymization, Big data, Cloud Computing, Map Reduce, Programming Model, Scalability.
Scope of the Article: Cloud Computing.