Research Article | OPEN ACCESS
Privacy Preserving Data Mining
1A.T. Ravi and 2S. Chitra
1Department of Computer Science and Engineering, SSM College of Engineering, Komarapalayam, India
2Department of Computer Science and Engineering, Er. Perumal Manimekalai College of
Engineering, India
Research Journal of Applied Sciences, Engineering and Technology 2015 8:616-621
Received: October 10, 2014 | Accepted: November 3, 2014 | Published: March 15, 2015
Abstract
Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information or to anonymize them at an uncertainty level. This study uses PPDM with adult dataset to investigate effects of K-anonymization for evaluation metrics. This study uses Artificial Bee Colony (ABC) algorithm for feature generalization and suppression where features are removed without affecting classification accuracy. Also k-anonymity is accomplished by original dataset generalization.
Keywords:
Adult dataset, Artificial Bee Colony (ABC) algorithm, data mining, K-anonymization, Privacy Preserving Data Mining (PPDM),
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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