Article Fingerprint
ReserarchID
CSTSDEGHH28
Data mining provides tools to convert a large amount of knowledge data which is user relevant. But this process could return individual’s sensitive information compromising their privacy rights. So, based on different approaches, many privacy protection mechanism incorporated data mining techniques were developed. A widely used micro data protection concept is k-anonymity, proposed to capture the protection of a micro data table regarding re-identification of respondents which the data refers to. In this paper, the effect of the anonymization due to k-anonymity on the data mining classifiers is investigated. Naïve Bayes classifier is used for evaluating the anonymized and non-anonymized data.
J. Paranthaman. 2013. \u201cPerformance Evaluation of K-Anonymized Data\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C8): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
Total Score: 107
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: J.Paranthaman, Dr. T Aruldoss Albert Victoire (PhD/Dr. count: 1)
View Count (all-time): 243
Total Views (Real + Logic): 9338
Total Downloads (simulated): 2489
Publish Date: 2013 07, Wed
Monthly Totals (Real + Logic):
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
Data mining provides tools to convert a large amount of knowledge data which is user relevant. But this process could return individual’s sensitive information compromising their privacy rights. So, based on different approaches, many privacy protection mechanism incorporated data mining techniques were developed. A widely used micro data protection concept is k-anonymity, proposed to capture the protection of a micro data table regarding re-identification of respondents which the data refers to. In this paper, the effect of the anonymization due to k-anonymity on the data mining classifiers is investigated. Naïve Bayes classifier is used for evaluating the anonymized and non-anonymized data.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.