학술빅데이터 분석에 기반한 개인정보 침해의 예방적 이해

Purpose: As the socio-economic value of personal data rises in the AI era, this study aims to provide foundational insights for effectively preventing personal data breaches. Methods: We analyzed 34,004 Korean academic publications (1960–2022) on personal data using text mining techniques, including LDA topic modeling, to examine definitions, management frameworks, technical safeguards, and ethical and legal issues. Results: The volume of academic research on personal data grew steadily after the introduction of the big data concept in the 1990s, with notable spikes after major data breaches in the 2000s and the advent of recommendation systems in the 2010s. Keyword analysis shows that dominant themes include the explosion of data driven by the proliferation of the internet and smartphones, as well as the utilization and protection of personal data. Topic modeling reveals high interest in applying recommendation systems across sectors such as marketing, healthcare, finance, and education, along with developing intelligent personal data protection systems. Conclusion: Based on these findings, this study advocates a preventive approach and suggests an integrated strategy combining policy measures with technological safeguards. Specifically, implementing proactive, AI-driven personal data protection systems and raising public awareness are proposed to strengthen data security and foster a sustainable digital society.