ScholarWorks@중앙대학교

초록

The acceleration of digital transformation has made the personal information processing environment of companies increasingly complex and diverse. As a result, the amount of data collected through various personal information processing devices has increased, exacerbating the risk of personal information leaks and misuse. In Korea, the "Personal Information Protection Level Assessment" system is operated with the aim of improving the level of personal information protection in public institutions. However, the assessment is still conducted manually by specialized personnel, resulting in excessive time and manpower requirements, as well as limitations in terms of objectivity and consistency. This study aims to address these issues by developing a measurement tool for assessment automation based on the "Public Institution Personal Information Management Level Diagnosis" indicators, which are the preliminary steps of the personal information protection level assessment. Specifically, diagnostic items were classified into rule-based, machine learning (ML)-based, and form-based categories, and appropriate automation models were designed for each type. The proposed approach is expected to enhance reliability and consistency compared to existing evaluation methods and provide a substantial turning point for improving administrative efficiency and policy utilization in the future.

키워드