Thunder- D e ID : Accurate and Efficient De-identification Framework for K orean Court Judgments (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.

## introduction generally, court proceedings are open and accessible to the public. it is one of the key democratic principles enshrined in the constitutions of many countries, including south korea 1 . south korea is one of the countries with more stringent conditions that cover a broader range of personal identifiers to be anonymized in the court setting. before the publication of court decisions, the korean national court administration uses both manual and automated de-identification methods throughout four stages of processing and review (judicial policy research institute of @xcite . however, the current state of the de-identification procedure is not capable of handling court judgments at scale. we want to address the following three problems of the current state of the de-identification procedure in south korea. first, over-reliance on the manual method has been a major bottleneck, causing administrative strain and delaying publication of judgments. public accessibility of judgments has been significantly low in south korea, and the stagnant de-identification procedure is one of the reasons (national court administration of @xcite . second, the automatic de-identification tool's performance is surprisingly low. from 2019 to 2025, their overall accuracy merely spans 8 to 15% (national assembly of korea, 2019; national court administration of korea, 2025). finally, while existing law lays out the scope of deidentification, how personal identifiers are categorized and defined for administrative practice at the court is vague and especially unsuitable to be used for automated technical solutions. to overcome the above problems, this paper proposes thunder-deid, a dnn-and ner-based framework, which improves the accuracy, efficiency, and consistency of de-identifying court judgments. unlike a prompt-based approach using a large language model (llm), which often alters the original sentence structure in the process of deidentification task (e.g., "총 3명 (a total of three people)" altered to "총 명수 1 (a total of one person)"), the token-level classification method of thunder-deid eliminates such risks of sentence and context distortion (see appendix h). moreover, due to privacy and information security concerns, the use of api-based llm services, such as chatgpt, is restricted in many of the key government institutions in korea @xcite . to create a trainable dataset from anonymized and unannotated court judgment data, we first manually label 6,700 civil, criminal, and administrative law cases that cover a broad spectrum of scenarios in civil, criminal, and administrative law. from these annotations, which identified 48,306 named entities, we establish a hierarchical categorization scheme for pii that aligns with relevant laws and practices and is suitable for model training. for each of the 729 labels in the pii scheme, we curate a corresponding list of entity mentions to generate model training data, as illustrated in figure 1 . furthermore, we design a de-identification pipeline for the dnn-based language model, incorporating a specialized tokenizer that leverages the unique characteristics of the korean language. the approach used in this paper may offer valuable insights for other jurisdictions looking to efficiently anonymize large volumes of court decisions. the contributions of this paper are summarized as follows: among others, there are many de-identification studies in health information. in the usa, deidentification in the medical field is guided by the health insurance portability and accountability act (hipaa) (u.s. department of @xcite , which defines two main strategies for compliance: the safe harbor method and expert determination @xcite . the safe harbor method requires the removal of 18 identifiers called personal health information (phi). alternatively, expert determination relies on a statistical or scientific method to ensure minimal re-identification risk. in europe, the general data protection regulation (gdpr) @xcite guides the de-identification of personal information in medical data. in this paper, we propose a threetiered pii scheme for the de-identification of court judgment. ## methods there are three challenges unique to constructing datasets for the de-identification of court judgments in south korea. first, since de-identification of court judgments prior to publication is a legal obligation of judicial institutions foot_0 , and only the fully anonymized judgments are available for external use, we need a method to generate datasets using anonymized and unannotated data. second, there are legal rules to define categories of personal identifiers to be anonymized foot_1 . however, they are not detailed enough to cover various attributes related to the persons involved in proceedings. they merely provide a direct identifier category and a broad quasi-identifier category that includes any other information that can identify the individual. finally, since the south korean judiciary heavily relies on manual de-identification, which is timeconsuming (national assembly of korea, 2019; national court administration of korea, 2025), a large volume of court rulings that can immediately be used as a legal corpus for training is not available. ## experiments this section evaluates thunder-deid and the experimental methodology. ## conclusion in this paper, we propose a dnn-based solution, referred to as thunder-deid, for ner aimed at improving the efficiency and consistency of de-identifying court judgments. we address the complex challenges currently faced in the deidentification process within the korean judiciary. our work includes the development of the first korean legal dataset, which contains 6,700 judgments from civil, criminal, and administrative cases, encompassing a total of 48,306 labeled named entities. we also introduce a three-tiered annotation scheme for pii, which systematically categorizes a wide variety of personal identifiers. furthermore, we provide a comprehensive list of entity mentions that can be used to replace the 729 token-level labels found in the training dataset. in addition, we outline a tokenization method for the training data generated from these replacements. our experimental results show that thunder-deid achieves stateof-the-art performance in the de-identification of court judgments.