Performance Evaluation of K-Anonymized Data

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J. Paranthaman
J. Paranthaman
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J.Paranthaman
J.Paranthaman
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Dr. T Aruldoss Albert Victoire
Dr. T Aruldoss Albert Victoire
α University College of Engineering

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Performance Evaluation of K-Anonymized Data

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Abstract

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.

References

12 Cites in Article
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Version of record

v1.2

Issue date

July 31, 2013

Language
en
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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.

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Performance Evaluation of K-Anonymized Data

J.Paranthaman
J.Paranthaman
Dr. T Aruldoss Albert Victoire
Dr. T Aruldoss Albert Victoire

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