--- title: "Detecting Sensitive Personal Information in Japanese" description: "The authors build one from a Japanese Common Crawl subset (llm-jp-corpus-v3). 0.32 precision. 3 references." authors: ["Rei Minamoto", "Yusuke Oda", "Daisuke Kawahara"] year: 2026 venue: "Findings of the Association for Computational Linguistics: ACL 2026" source: "https://aclanthology.org/2026.findings-acl.653/" doi: "10.18653/v1/2026.findings-acl.653" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/minamoto-2026-detecting-sensitive-personal-information-japanese/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/minamoto-2026-detecting-sensitive-personal-information-japanese/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/29/) / Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models # Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models **Authors:** [Rei Minamoto](https://md.research.elamur.ai/free/en/authors/rei-minamoto/), Yusuke Oda and [Daisuke Kawahara](https://md.research.elamur.ai/free/en/authors/daisuke-kawahara/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.653/) · [PDF](https://aclanthology.org/2026.findings-acl.653.pdf) · [DOI 10.18653/v1/2026.findings-acl.653](https://doi.org/10.18653/v1/2026.findings-acl.653) · [OpenAlex](https://openalex.org/W7166834154) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary The paper tackles a gap in Japanese LLM data curation: web-crawled pre-training corpora may contain sensitive personal data that Japan's APPI classifies as special care-required personal information (SCPI), yet no Japanese work had studied detecting it, and legal restrictions mean no public SCPI dataset exists. The authors build one from a Japanese Common Crawl subset (llm-jp-corpus-v3). They first keep texts that mention person names, using an NER model. Small open LLMs (Swallow 8B, Gemma 9B) then screen these candidates, and only texts both models agree on go to GPT-OSS (120B) for final labeling. Annotators fix outputs the large model gets wrong. Using this data, they train fast classifiers, including traditional models such as SVC and LGBM. These models identify SCPI-related category information well and cost far less than LLM- or Transformer-based classification, so they suit a first filtering pass over huge corpora. SHAP analysis shows that many influential features are irrelevant noise. A further difficulty is linking the identifying information to the sensitive content, which often sit far apart in a text. The dataset stays private for legal and ethical reasons. ## Key points - There are 12 labels in total: a non-SCPI class plus the 11 SCPI categories defined by APPI and its Cabinet Order, namely creed, race, disability, social status, medical history, diagnostic information, medical treatment, criminal record, criminal proceedings, crime victimization, and juvenile protection. For analysis purposes only, an additional LGBT category was introduced. - The source corpus was 2.61M texts sampled from llm-jp-corpus-v3/ja/ja_cc/level0, averaging 2,474 characters. NER found person names in 1.06M of them. - Combining the small models raised screening precision. Swallow alone kept 9,220 texts at 0.32 precision, while agreement between Swallow and Gemma (0.8 confidence threshold) kept 2,373 texts at 0.55. - All LLM annotation ran locally under APPI's academic-research exception. Neither the data nor the classifier is publicly released, partly over misuse concerns. - Limitations: - Only name-based identification is covered. - Race, creed, and social status were not evaluated. - The classifier cannot learn any SCPI that the annotating LLMs fail to detect. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [National Institute of Informatics](https://md.research.elamur.ai/free/en/labs/national-institute-of-informatics/) — Research institute, Tokyo, Japan · 38 works · 3,247 citations - [Waseda University](https://md.research.elamur.ai/free/en/universities/waseda-university/) — University, Tokyo, Japan · 21 works · 584 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 626 works 🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works ## Cite Cite as: Rei Minamoto, Yusuke Oda and Daisuke Kawahara. 2026. Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models. Findings of the Association for Computational Linguistics: ACL 2026. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{minamoto2026detecting, title = {{Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models}}, author = {Rei Minamoto, Yusuke Oda and Daisuke Kawahara}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2026}, year = {2026}, doi = {10.18653/v1/2026.findings-acl.653}, url = {https://doi.org/10.18653/v1/2026.findings-acl.653} } ``` - [← previous Detecting Risky Behavior Related to Alcohol and Drug Use within Adolescents' Private Messenger Conversations](https://md.research.elamur.ai/free/en/papers/plhak-2026-detecting-risky-behavior-related-alcohol/) - [next → Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks](https://md.research.elamur.ai/free/en/papers/dixit-2026-detecting-speculative-language-biomedical-texts/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/29/) ## References 3 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. [Optuna: A next-generation hyperparameter optimization framework](https://md.research.elamur.ai/free/en/papers/akiba-2019-optuna-next-generation-hyperparameter-optimization/). In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2. Jason Ansel, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, and 30 others. 2024… — [PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) 3. California Legislature. 2024. California civil code section 1798.140. Accessed 2025-05-11. [find](https://scholar.google.com/scholar?q=California+Legislature%2E+2024%2E+California+civil+code+section+1798%2E140%2E+Accessed+2025%2D05%2D11%2E) *Updated 1 November 2026*