This study aims to enhance the effectiveness of the personal information file registration and disclosure system in public institutions by proposing a standardization framework for the registration structure, required items, and overall disclosure for...
This study aims to enhance the effectiveness of the personal information file registration and disclosure system in public institutions by proposing a standardization framework for the registration structure, required items, and overall disclosure format. Although Article 32 of the Personal Information Protection Act stipulates that public institutions must register and disclose personal information files, the current Personal Information Protection Portal revealed inconsistencies: even for identical tasks, file names, legal bases, and purposes varied across institutions, and the downloaded data exhibited non-standardized formats such as split cells. In this study, personal information file data registered by metropolitan governments were collected and refined, and the classification system was reorganized based on the Local Business Reference Model (LBRM). The file name, legal basis, purpose of use, processing method, and retention period were compared and analyzed to derive a standardized list grounded in the following criteria: priority of legal basis, reflection of existing registration patterns, and application of public data standard terminology. As a result, standardization of files with similar characteristics improved the accuracy and consistency of registration information, and the structured disclosure format contributed to strengthening data subjects’ rights. Survey results also indicated that 86.6% of respondents agreed that standardization positively influences administrative efficiency and protection of data subjects’ rights. This study provides the foundation for transforming the personal information file system from a formal disclosure mechanism into a substantive management framework, and further suggests the potential for disclosure through generative AI, enabling data subjects to easily identify how their personal information is processed.