A Review on ‘Privacy Preservation Data Mining’

March 2018
Vol-4, Issue-2
Paper ID: 7479
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Data Mining Cryptography Randomization Quasi Attribute Sensitive Attribute perturbation data mining privacy preservation Slicing.
Abstract
Privacy Preserving Data Mining (PPDM) is used to mine the potential valuable knowledge without revealing the personal information of the individuals. Now a days due to data privacy has become a major concern as quasi and sensitive attribute in terms of privacy needs a lot of research existing researches on privacy of quasi and sensitive attribute are time consuming and also are not space efficient so by this research to trying different techniques used in various papers for establish that which method is more convenient. It is often highly valuable for organizations to have their data analyzed by external agents.. In the era of information society, sharing and publishing data has been a common practice for their wealth of opportunities. However, the process of data collection and data distribution may lead to disclosure of their privacy. Privacy is necessary to conceal private information before it is shared, exchanged or published. PPDM has thus has received a significant amount of attention in the research literature in the recent years. Various methods have been proposed to achieve the expected goal. In this paper we have given a brief discussion on different dimensions of classification of privacy preservation techniques.

Author Information

# Name Institute / Affiliation
1 Shivangi Modhiya G.H.Patel College of Engineering & Technology
2 Jay Vala G.H.Patel College of Engineering & Technology
3 Prem Balani G.H.Patel College of Engineering & Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style
Modhiya, Shivangi, Vala, Jay, & Balani, Prem (2018). A Review on ‘Privacy Preservation Data Mining’. International Journal of Advance Research and Innovative Ideas In Education, 4(2), 160-164.
MLA Style
Modhiya, Shivangi, et al. "A Review on ‘Privacy Preservation Data Mining’." International Journal of Advance Research and Innovative Ideas In Education, vol. 4, no. 2, 2018, pp. 160-164.
IEEE Style
Shivangi Modhiya, Jay Vala, and Prem Balani, "A Review on ‘Privacy Preservation Data Mining’," International Journal of Advance Research and Innovative Ideas In Education, vol. 4, no. 2, pp. 160-164, 2018.
Vancouver Style
Modhiya Shivangi, Vala Jay, Balani Prem. A Review on ‘Privacy Preservation Data Mining’. International Journal of Advance Research and Innovative Ideas In Education. 2018;4(2):160-164.
Harvard Style
Modhiya, Shivangi, Vala, Jay, & Balani, Prem (2018) 'A Review on ‘Privacy Preservation Data Mining’', International Journal of Advance Research and Innovative Ideas In Education, 4(2), pp. 160-164.
Chicago Style
Modhiya, Shivangi, Jay Vala, and Prem Balani. "A Review on ‘Privacy Preservation Data Mining’." International Journal of Advance Research and Innovative Ideas In Education 4, no. 2 (2018): 160-164.
Turabian Style
Modhiya, Shivangi, Jay Vala, and Prem Balani. "A Review on ‘Privacy Preservation Data Mining’." International Journal of Advance Research and Innovative Ideas In Education 4, no. 2 (2018): 160-164.

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