Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Ileas Pramanik ◽  
Raymond Y. K. Lau ◽  
Md Sakir Hossain ◽  
Md Mizanur Rahoman ◽  
Sumon Kumar Debnath ◽  
...  
Keyword(s):  
Big Data ◽  
Critical Analysis ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Privacy Preserving

Privacy-Preserving in Big Data Analytics: State of the Art

2020 ◽  
Author(s):  
Hidayath Ali Baig ◽  
Dr. Yogesh Kumar Sharma ◽  
Syed Zakir Ali
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Privacy Preserving

Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 283-295
Author(s):  
Ajmeera Kiran ◽  
Vasumathi Devara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Input Data ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Fruit Fly ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Convolution Process ◽  
Fuzzy C Means Clustering

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications. Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods. Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks. Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset. Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.

scholarly journals Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

2017 ◽  
Vol 13 (4) ◽  
pp. 1891-1899 ◽  
Author(s):  
Zhihan Lv ◽  
Houbing Song ◽  
Pablo Basanta-Val ◽  
Anthony Steed ◽  
Minho Jo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Future Research ◽  
Next Generation ◽  
Research Topics
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