• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.340-343
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• 2016

The Korean government published the personal information de-identification guideline on June 2016, which were made by related government ministries. The guideline's objective is that the invigoration of Korean bigdata industry on personal information protection under the current laws. However, if there is some unreasonable method or process in the guideline, it can be an obstacle to bigdata analysis. This article will review the guideline to find defects in methods and processes of de-identification evaluation, de-identification support and data-linkage and then propose the best solutions to improve them. Lastly, this article will mention how these solutions can invigorate Fintech industry.

• Smart Media Journal
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• v.5 no.4
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• pp.83-89
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• 2016

Guidelines for protecting personal information are already in progress in USA, UK and other countries and announced many guideline like HIPPA. However In Our national environment, we does not have specialized guideline in national medical industries. This thesis suggest De-indentification method in South Korea by referring 'bigdata De-identification Guideline by Ministry of Science, ICT and Future Planning (2015)', ICO in U. K and IHE, NIST, HIPPA in U. S. A. We suggest also correlation between Guidelines. Corelation means common techniques in three guidelines (IHE, NIST, HIPPA in U. S. A). As Point becomes closer five points, We recommend that technique to national medical institute for De-Identification. We hope this thesis makes the best use of personal information's development in National medical institute.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.325-340
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• 2020

Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.

• Physical Therapy Rehabilitation Science
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• v.8 no.2
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• pp.99-110
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• 2019

Objective: The aim of the present study was to develop a valid and reliable scale that measures the healthy life styles among young adults. Design: A methodological study design was employed to develop and validate the Healthy Lifestyle Screening Tool (HLST). Methods: The validity and reliability of the HLST were established in accordance with DeVellis' 8 steps guideline for tool development. The question items were generated based on literature reviews and interviews, which were then classified into 12 categories. The HLST was administered to 272 students attending a Korean university. The reliability was tested using Cronbach's alpha. The validity of the scale was examined with the mean inter-item correlations (MIIC) and factor analysis, and was also examined for content validity by experts. Results: The reliability of the HLST was found to be acceptable, as indicated by a Cronbach's alpha of 0.71. In the validity test, items with less than 80% "agreement" ratings on the content validity index by experts were revised. The MIIC values were greater than 0.25. A factor analysis of 36 items extracted 9 factors (i.e., four items per factor), which together explained 50.4% of the variance. The HLST consists of 36 items that measure 9 factors based on a 4-point Likert rating scale, with 4 items per factor, as follows: sunlight, water, air, rest, exercise, nutrition, temperance, trust, and general physical condition. High scores on the HLST are indicative of a healthy lifestyle (HL). Conclusions: The HLST is a valid and reliable scale that can be used to measure HL among young adults. Identification of HL by using the HLST can provide guidance to integrated therapeutic approaches along with conventional physical therapy.