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Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data

by Ramya Shree A. N. Kiran P.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 28
Year of Publication: 2019
Authors: Ramya Shree A. N. Kiran P.
10.5120/ijca2019919091

Ramya Shree A. N. Kiran P. . Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data. International Journal of Computer Applications. 178, 28 ( Jun 2019), 4-9. DOI=10.5120/ijca2019919091

@article{ 10.5120/ijca2019919091,
author = { Ramya Shree A. N. Kiran P. },
title = { Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 28 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 4-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number28/30711-2019919091/ },
doi = { 10.5120/ijca2019919091 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:39.297541+05:30
%A Ramya Shree A. N. Kiran P.
%T Privacy Preserving Unstructured Data Publishing (PPUDP) Approach for Big Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 28
%P 4-9
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The current trend related to Computer Industry is Big Data Analytics (BDA). The key attributes of Big Data are Volume, Variety, and Velocity. Big Data Analytics mainly focuses on Data collection from heterogeneous sources, Knowledge extraction from collected data and Data storage. In all these process of knowledge discovery from Big Data, Privacy of Big Data is very crucial. As it comprises of different types of data like structured data, unstructured data and semi structured data, a variety of techniques available for preserving privacy for structured and *semi structured data. The major issue with unstructured data is data publishing i.e. lack of preserving privacy because it may contain heterogeneous data like audio, video and text. The Big Data analytics normally carried out by third party, the data provider can be able to classify data as secured or not secured prior to data publishing for Data Analytics and it involves classification of huge volumes of data. The issue can be addressed using Privacy Preserving Unstructured Data Publishing for Big Data.

References
  1. F. S. Gharehchopoh and Z. A. Khalifelu (2010) Analysis and Evaluation of Unstructured Data, IEEE Access.
  2. Lei Xu, Chunxiao Jiang,Jian Wang, Jian Yuan, & Yong Ren (2014) Information Security in Big Data: Privacy and Data Mining. V2, IEEE Open Access Journal.
  3. C. C. Aggarwal and S. Y. Philip (2012) A General Survey of Privacy-Preserving Data Mining Models and Algorithms. New York, NY, USA: Springer-Verlag.
  4. Ricardo Mendes, Joao P Vilela (2017) PPDM methods, metrics and applications. IEEE Access, Volume 5.
  5. F. Ronen and S. James (2006) The Text Mining Handbook: Advanced Approaches in Analyzing Data, vol. 1, Cambridge University Press.
  6. Manning C, Schutze H (1999) Foundations of Statistical Natural Language Processing, MIT Press, Cambridge, MA.
  7. Kiran P (2012) A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing. International Journal of Computer Applications (0975-8887) Volume 53–No.18.
  8. Han, M. Kamber, and J. Pei (2006) Data Mining: Concepts and Techniques.San Mateo, CA, USA: Morgan Kaufmann Publishers.
  9. M. B. Malik, M. A. Ghazi, and R. Ali (2012) Privacy preserving data mining techniques: Current scenario and future prospects,'' in Proc. 3rd Int. Conf.Comput. Commun. Technol. (ICCCT), pp. 26-32.
  10. F.Popowich(2005) Using Text Mining and Natural Language Processing for Health Care Claims processing , SIGKDD Explorations, vol. 7, Issue 1, pp. 59-66.
  11. Gal, Tamas S, Z Chen, and A Gangopadhyay ((2008) A privacy protection model for patient data with multiple sensitive attributes. IGI Global 28–44.
  12. Dash .S, Vijayakumar K, Panigrahi B, Das S (2017) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore.
  13. Privacy-Preserving Data Mining: Methods, Metrics, and Applications ,IEEE Access, Volume 5,2017
  14. Privacy Preserving Data Mining Algorithms by Data Distortion,Wu Xiao-dan , Yue Dian-min ,Liu Feng-li , Wang Yun-feng ,Chu Chao-Hsien, ,International Conference on Management Science and Engineering, 04 September 2007,IEEE Access,Volume5
  15. Naive Bayes Classifier, Lecture Notes in Computer Science, 2005, Vol 3584
  16. Privacy preserving data mining - „A state of the art‟ Mamta Narwaria , Suchita Arya 3rd International Conference on Computing for Sustainable Global Development, 2017 . S. Matwin, "Privacy-preserving data mining techniques: Survey and challenges", in Discrimination and Privacy in the Information Society, Berlin, pp. 209-221, 2013.
  17. S.-W. Chen et al.,Confidentiality protection of digital health records in cloud computing,‟‟ J. Med. Syst., vol. 40, no. 5, p. 124, 2016.
  18. J. Vincent, W. Pan, and G. Coatrieux, „„Privacy protection and security in ehealth cloud platform for medical image sharing,‟‟ in Proc. IEEE 2nd Int. Conf. Adv. Technol. Signal Image Process. (ATSIP), Mar. 2016, pp. 93–96.
  19. M. S. Kiraz, Z. A. Genç, and S. Kardas, „„Security and efficiency analysis of the Hamming distance computation protocol based on oblivious transfer,‟‟ Secure. Commun. Netw., vol. 8, no. 18, pp. 4123–4135, 2015.
  20. J. Zhou, Z. Cao, X. Dong, and X. Lin, „„PPDM: A privacy-preserving protocol for cloud-assisted e-healthcare systems,‟‟ IEEE J. Sel. Topics Signal Process., vol. 9, no. 7, pp. 1332–1344, Oct. 2015.
  21. P. J. McLaren et al., „„Privacy-preserving genomic testing in the clinic: A model using HIV treatment,‟‟ Genet. Med., vol. 18, no. 8, pp. 814–822, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

PPUDM SDL

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