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Privacy Preserving Data Mining Methods and Metrics Analysis

프라이버시 보존형 데이터 마이닝 방법 및 척도 분석

  • 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)
  • 홍은주 (공주대학교 융합과학과) ;
  • 홍도원 (공주대학교 응용수학과) ;
  • 서창호 (공주대학교 응용수학과)
  • Received : 2018.08.07
  • Accepted : 2018.10.20
  • Published : 2018.10.28

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

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

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