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http://dx.doi.org/10.14400/JDC.2018.16.10.445

Privacy Preserving Data Mining Methods and Metrics Analysis  

Hong, Eun-Ju (Dept. of Convergence Science, Kongju National University)
Hong, Do-won (Dept. of Applied Mathematics, Kongju National University)
Seo, Chang-Ho (Dept. of Applied Mathematics, Kongju National University)
Publication Information
Journal of Digital Convergence / v.16, no.10, 2018 , pp. 445-452 More about this Journal
Abstract
In a world where everything in life is being digitized, the amount of data is increasing exponentially. These data are processed into new data through collection and analysis. New data is used for a variety of purposes in hospitals, finance, and businesses. However, since existing data contains sensitive information of individuals, there is a fear of personal privacy exposure during collection and analysis. As a solution, there is privacy-preserving data mining (PPDM) technology. PPDM is a method of extracting useful information from data while preserving privacy. In this paper, we investigate PPDM and analyze various measures for evaluating the privacy and utility of data.
Keywords
Privacy; Data Mining; Privacy-Preserving Data Mining; Metric; Utility;
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