Sungeun Hahm — accepted AI-conference papers

2025

Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments

Sungeun Hahm, Heejin Kim, Gyuseong Lee, Hyunji M. Park, Jaejin Lee

EMNLP 2025

To ensure a balance between open access to justice and personal data protection, the South Korean judiciary mandates the de-identification of court judgments before they can be publicly disclosed. However, the current de-identification process is inadequate for handling court judgments at scale whil

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2024

Generalizing Clinical De-identification Models by Privacy-safe Data Augmentation using GPT-4

Woojin Kim, Sungeun Hahm, Jaejin Lee

EMNLP 2024main

De-identification (de-ID) refers to removing the association between a set of identifying data and the data subject. In clinical data management, the de-ID of Protected Health Information (PHI) is critical for patient confidentiality. However, state-of-the-art de-ID models show poor generalization o…

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