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A study on Data Context-Based Risk Measurement Method for Pseudonymized Information Processing

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(6), pp.53-63
  • DOI : 10.9708/jksci.2022.27.06.053
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 10, 2022
  • Accepted : June 13, 2022
  • Published : June 30, 2022

Kim Donghyun 1

1한국인터넷진흥원

Accredited

ABSTRACT

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.

Citation status

* References for papers published after 2022 are currently being built.