• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.53-63
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• 2022

Recently, as digital transformation due to the COVID-19 pandemic accelerates, data to improve individual quality of life is being used in large quantities, and more reinforced non-identification processing procedures are required to utilize the most valuable personal information among data. In Korea, procedures for de-identification measures are presented through amendments to laws and guidelines, but there is no methodology to measure the level of de-identification in the field due to ambiguous processing standards and subjective risk measurement methods. This paper compares and analyzes the current status of policy and guidelines related to de-identification measures proposed at home and abroad to derive complementary points, suggests a data context-based risk measurement method centered on pseudonymized information processing, and verifies its validity. As a result of verification through Delphi survey and focus group interview (FGI), it was confirmed that the need for the proposed methodology and the validity of the indicators were high.