An Effective Security and Privacy in Big Data analytics Using Machine Learning
Shrihari M R, Dr. Manjunath.T.N
Big Data Analytics, Machine learning Big Data, MapReduce, NoSQL, Interactive Analytics, Privacy, Security
The Conservation of privacy largely relies on technological limitations on the ability to extract, analyze, and correlate potentially sensitive data sets. However, advances in Big Data analytics provide tools to extract and utilize this data, making violations of privacy easier. As a result, along with implementing Big Data tools, it is necessary to create safeguards to prevent abuse In addition to privacy; data used for analytics may include regulated information or intellectual property. Moreover, current efforts aimed at improving and extending Map Reduce to address identified challenges are presented. Accordingly, by identifying issues and challenges Map Reduce faces when handling Big Data, this study encourages future Big Data research System architects must ensure that the data is protected and used only according to regulations. The scope of this document is on how Big Data can improve information security best practices. Identifying the best practices in Big Data privacy and increasing awareness of the threat to private information Machine learning as a data science to uncover patterns and hidden insights is not entirely a new concept. It has been in play with the use of neural networks starting in the 1980’s. The question therefore is, “Why is there a big buzz around machine learning today?”The answer deception in the fact that development in technology and science has enabled game-changing differences in how machine learning algorithms have evolved and being applied. For example, traditionally, human-generated rule sets were the most prevalent approach in fraud management and still continue to be in practice today. But the required leap in computing power and accessibility of big data over the last five years has disrupted how data is being used to identify and prevent fraud. In the Big Data community, Map Reduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high capacity of the Map Reduce model which allows for extremely equivalent and disseminated execution over a large number of computing nodes. This paper identifies Map Reduce issues and challenges in handling Big
Article Details
Unique Paper ID: 145255

Publication Volume & Issue: Volume 4, Issue 8

Page(s): 188 - 197
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Volume 5 Issue 7

Last Date 25 December 2018

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