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http://dx.doi.org/10.5392/JKCA.2019.19.05.553

Data Quality Measurement on a De-identified Data Set Based on Statistical Modeling  

Chun, Heuiju (동덕여자대학교)
Yi, Hyun Jee (동국대학교)
Yeon, Kyupil (호서대학교)
Kim, Dongrae ((주)이지서티)
Publication Information
The Journal of the Korea Contents Association / v.19, no.5, 2019 , pp. 553-561 More about this Journal
Abstract
In this study, the method of quality measurement for the statistical usefulness of de-identified data was examined in terms of prediction accuracy by statistical modeling. In the era of the 4th industrial revolution, effective use of big data is essential to innovation through information and communication technology, but personal information issues are constrained to actively utilize big data. In order to solve this problem, de-identification guidelines have been established and the possibility of actual re-identification of personal information has become very low due to the utilization of various de-identification methods. On the other hand, strong de-identification can have side effects that degrade the usefulness of the data. We have studied the quality of statistical usefulness of the de-identified data by KLT model which is a representative de-identification method, A case study was conducted to see how statistical accuracy of prediction is degraded by de-identification. We also proposed a new measure of data usefulness of the de-identified data by quantifying how much data is added to the de-identified data to restore the accuracy of the predictive model.
Keywords
Personal Information; Data Quality; De-identification; Predictive Model; KLT-Model;
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