Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
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
while adhering to strict legal requirements. Additionally, the legal
definitions and categorizations of personal identifiers are vague and not
well-suited for technical solutions. To tackle these challenges, we propose a
de-identification framework called Thunder-DeID, which aligns with relevant
laws and practices. Specifically, we (i) construct and release the first Korean
legal dataset containing annotated judgments along with corresponding lists of
entity mentions, (ii) introduce a systematic categorization of Personally
Identifiable Information (PII), and (iii) develop an end-to-end deep neural
network (DNN)-based de-identification pipeline. Our experimental results
demonstrate that our model achieves state-of-the-art performance in the
de-identification of court judgments.
