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Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

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Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

by Alpa Shah, Ravi Gulati
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 12
Year of Publication: 2016
Authors: Alpa Shah, Ravi Gulati
10.5120/ijca2016909006

Alpa Shah, Ravi Gulati . Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey. International Journal of Computer Applications. 137, 12 ( March 2016), 40-46. DOI=10.5120/ijca2016909006

@article{ 10.5120/ijca2016909006,
author = { Alpa Shah, Ravi Gulati },
title = { Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 12 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number12/24331-2016909006/ },
doi = { 10.5120/ijca2016909006 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:13.342600+05:30
%A Alpa Shah
%A Ravi Gulati
%T Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 12
%P 40-46
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy has become crucial in knowledge based applications. Proper integration of individual privacy is essential for data mining operations. This privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers, to name a few. The main categorization of Privacy Preserving Data Mining (PPDM) techniques falls into Perturbation, Secure Sum Computations and Cryptographic based techniques. There exist tradeoffs between privacy preservation and information loss for generalized solutions. The authors of the paper present an extensive survey of PPDM techniques, their classification and give a preliminary implication of technique to be used under specific scenarios.

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Index Terms

Computer Science
Information Sciences

Keywords

PPDM Perturbation Cryptography SMC Randomization Condensation Anonymization

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