CFP last date
22 April 2024
Reseach Article

A Secure Data Evaluation and Publishing Technique for Big Data

by Pratiksha Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 29
Year of Publication: 2020
Authors: Pratiksha Patil
10.5120/ijca2020920828

Pratiksha Patil . A Secure Data Evaluation and Publishing Technique for Big Data. International Journal of Computer Applications. 175, 29 ( Nov 2020), 17-23. DOI=10.5120/ijca2020920828

@article{ 10.5120/ijca2020920828,
author = { Pratiksha Patil },
title = { A Secure Data Evaluation and Publishing Technique for Big Data },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 29 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number29/31633-2020920828/ },
doi = { 10.5120/ijca2020920828 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:39:47.298418+05:30
%A Pratiksha Patil
%T A Secure Data Evaluation and Publishing Technique for Big Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 29
%P 17-23
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The number of applications is become large now in these days which are dealing with thousands of users in a second. Therefore, the data is such application is also collected and processed in large quantity. To deal with such data the big data technology is used that is combination of software and hardware for efficient data processing. The aim of the proposed work to address the different privacy and content sensitivity issues in big data environment. In addition, of that the effort is made for improving the content to prevent the data leakage during the content publishing in public domain. Therefore, the proposed work is contributed for designing an attribute key encryption technique that works on random attribute selection policy. The selected attribute is used for common key generation, which is used for the shared files. To generate the key for encryption the MD5 algorithm is used. Additionally for the encryption the efficient algorithm namely the AES algorithm is used. Secondly for identifying the sensitive content on the user’s text the NLP (natural language processing) based technique is applied. That technique is used to extract the part of speech information from the text and to identify the noun from the text. That are the target data which is need to be encrypted. After encryption of the target text, the data is again reformed for publishing. To implement the entire scenario the web application is used which is usage the Hadoop storage for preserving the data. After implementation, the performance of the system measured in terms of time and space complexity. According to the results, the performance of system found acceptable.

References
  1. Liang, Kaitai, Willy Susilo, and Joseph K. Liu, "Privacy-preserving ciphertext multi-sharing control for big data storage", IEEE transactions on information forensics and security 10.8 (2015): 1578-1589.
  2. “Big Data: What it is and why it matters”, online available at: http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
  3. “What is Big Data? The Basics – Meaning and Usage”, the windows club, online available at: http://www.thewindowsclub.com/what-is-big-data
  4. Research Trends: Special Issue on Big Data, 30 September 2012
  5. Available online at: http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
  6. Kuchipudi Sravanthi and Tatireddy Subba Reddy, “Applications of Big data in Various Fields”, (IJCSIT) International Journal of Computer Science and Information Technologies, Volume 6 (5) 2015, pp. 4629-4632
  7. Greveler, Ulrich, Benjamin Justus, and Dennis Loehr, "A Privacy Preserving System for Cloud Computing", 2011 IEEE 11th International Conference on Computer and Information Technology (CIT), IEEE, 2011.
  8. Benjamin C. M. Fung, Ke Wang, Rui Chen, Philip S. Yu, "Privacy‐preserving data publishing: A survey of recent developments," In: ACM Computing Surveys (CSUR), Vol. 42, pp 1‐53, 2010.
  9. S. Hansell, "AOL removes search data on vast group of web users," New York Times, 2006.
  10. Divyakant Agrawal, Amr El Abbadi, and Shiyuan Wang, "Secure and privacy-preserving data services in the cloud: A data centric view." Proceedings of the VLDB Endowment 5, Number 12 (2012): 2028-2029.
  11. V. Abricksen, “A Survey on Cloud Computing and Cloud Security Issues”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622International Conference on Humming Bird (01st March 2014).
  12. Mohammad Asadullah and R. K. Choudhary, “Data Outsourcing Security Issues and Introduction of DOSaaS in Cloud Computing”, International Journal of Computer Applications (IJCA), PP. 40-45, Volume 85 – No 18, January 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Privacy preserving big data data publishing data leakage NLP POS