提出首个韩语司法文书脱敏框架，兼顾法律合规与高效处理
Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments
- 构建首个韩语司法文书标注数据集，明确实体类型
- 提出系统化个人信息分类体系，适配法律定义
- 基于深度神经网络实现端到端精准脱敏，性能领先
为平衡司法公开与个人数据保护，韩国司法机构要求公开前对判决文书进行脱敏。然而现有流程难以规模化处理判决文书，且法律中对个人标识信息的定义模糊，不适用于技术实现。为此，我们提出Thunder-DeID脱敏框架，符合相关法律与实践。具体包括：(i) 构建并发布首个含标注判决文书及实体提及列表的韩语法律数据集；(ii) 提出系统化的个人可识别信息（PII）分类体系；(iii) 开发基于深度神经网络（DNN）的端到端脱敏管道。实验表明，该模型在判决文书脱敏任务中达到当前最优性能。
原文摘要 · Abstract (English)
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.
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