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Sensitive Data Detection Using NN and KNN from Big Data

  • Binod Kumar Adhikari
  • Wan Li Zuo
  • Ramesh Maharjan
  • Lin Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

This paper focuses on the determination of sensitive data from huge mass of data collected from social network, cloud drives, local repository files etc. With the advancement of technology, numerous technologies have emerged and are actively being used in extracting useful and critical information about criminal activities from big data that get accumulated due to the use of communicating devices and applications. Numerous reduction techniques and data retrieval algorithm have been invented to extract sensitive information from accumulated data of criminals to prevent future criminal activities and to control unexpected events. In this paper, two different reduction techniques – Neural Network and K-Nearest Neighbor algorithms are used. Experiments for both algorithms were done in the similar environment by changing data size and node numbers in the processing cluster. From the experiment, it is found that Neural Network classification algorithm is more superior to retrieve sensitive data from big data than K- nearest neighbor algorithm.

Keywords

Terrorism Hadoop Distributed File System (HDFS) MapReduce Neurons Cluster 

Notes

Acknowledgment

Project supported by the National Nature Science Foundation of China (No. 60973040, No. 61602057), the Outstanding Young Talent Project of Jilin Providence (No. 2017052005954), the Key Scientific and Technology Projects of Jilin Province. (No. 20130206051GX).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Amrit Campus, Tribhuvan UniversityKathmanduNepal
  3. 3.School of Economic ManagementChangchun University of Science and TechnologyChangchunChina

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