Secure Sharing of Sensitive Data on a Big Data Platform

2019 ◽  
Vol 9 (2) ◽  
pp. 64-68
Author(s):  
Logeswari R ◽  
Manimaran V
Keyword(s):  
Big Data ◽  
Sensitive Data ◽  
Data Platform

scholarly journals Secure sensitive data sharing on a big data platform

2015 ◽  
Vol 20 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Xinhua Dong ◽  
Ruixuan Li ◽  
Heng He ◽  
Wanwan Zhou ◽  
Zhengyuan Xue ◽  
...  
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Sensitive Data ◽  
Data Platform

scholarly journals A Survey on Security of the Hadoop Framework in the Environment of Bigdata

2021 ◽  
Vol 2089 (1) ◽  
pp. 012031
Author(s):  
Saritha Gattoju ◽  
NagaLakshmi Vadlamani
Keyword(s):  
Big Data ◽  
Cost Effective ◽  
Corporate Management ◽  
Sensitive Data ◽  
Apache Hadoop ◽  
Huge Data ◽  
Data Platform ◽  
Healthcare Finance ◽  
Day To Day Operations ◽  
The Moment

Abstract The world is becoming increasingly digital at the moment. Every day, a significant amount of data is generated by everyone who uses the internet nowadays. The data are critical for carrying out day-to-day operations, as well as assisting corporate management in achieving their objectives and making the best judgments possible based on the information gathered. BigData is the process of merging many hardware and software solutions to deal with extremely huge amounts of data that surpass storage capability. It’s possible that large amounts of data will be generated. Hadoop systems are used in a variety of areas, including healthcare, finance, and government. insurance, and social media, in order to provide a quick and cost-effective big data solution. The Apache Hadoop is a framework for storing and processing data, managing, and distributing large amounts of information over a large number of server nodes. Here are some solutions that work on top of the Apache Hadoop stack to guarantee data security. To get a complete picture of the problem, we decided to conduct an investigation into existing security solutions for Apache Hadoop security in sensitive data which is stored on a huge data platform employing distributed computing on a cluster of commodity devices. The goal of this paper is to provide knowledge of security and Big Data issues.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

A monitoring framework for transparency and fairness in big data platform

2021 ◽  
Author(s):  
Karima Aslaoui Mokhtari ◽  
Salima Benbernou ◽  
Mourad Ouziri ◽  
Hakim Lahmar ◽  
Muhammad Younas
Keyword(s):  
Big Data ◽  
Data Platform ◽  
Monitoring Framework
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