Big Data Analysis and Deterministic Encryption Challenges

Aasim Shafi, Sahila Fareed Shah


Over the past decade, big data analysis has seen an exponential growth and will certainly continue to witness spectacular developments due to the emergence of new interactive multimedia applications and highly integrated systems driven by the rapid growth in information services and microelectronic devices. Up to yet, large no. of the existing mobile systems is mainly targeted to voice communications with low transmission rates. Big-Data has always been a part of our lives knowingly or unknowingly. This is a review on accessible big-data systems that include a set of tools and technique to load, extract, and improve dissimilar data while leveraging the immensely parallel processing power to perform complex transformations and analysis. Big-Data” technology faces a list of technical challenges.


Big-Data, Structured data, Un-Structured data, Random Encryption, Deterministic Encryption

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Shan Suthaharan, Machine Learning Models and Algorithms for Big Data Classification , Integrated Series in Information Systems 36 , Series Editors: Ramesh Sharda • Stefan Voß ,Springer.

J. Breckling,MIT Center for Transportation and logistics.

Du Zhang,” Inconsistencies in Big Data”, IEEE 978-1- 4799-0783-0/13, PP 61-67.

] Swag tam Das, Ajith Abraham, Senior Member, IEEE, and Amit Konar, “Automatic Clustering Using an Improved Differential Evolution Algorithm”, IEEE 2008, PP 218-237.

Carson Kai-Sang Leung, Richard Kyle MacKinnon, Fan Jiang, “Reducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data”, IEEE 2014, PP 315-322.

Vrushali Y Kulkarni,” Random Forest Classifiers: Survey

and Future Research Directions”, International Journal

of Advanced Computing, ISSN: 2051-0845, Vol.36,

Issue.1, and April 2013.



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