Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models
arXiv:2606.12114v1 Announce Type: new Abstract: Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information is therefore essential to ensure compliance with privacy regulations and prevent unintended information leakage. However, in contrast to English and other languages, research into sensitive personal information has been limited in the Japanese language. In this study, we focus on sensitive personal data defined as special care-required personal information (SCPI) under Japan's Act on the Protecti
The rapid deployment and increasing sophistication of large language models globally necessitate immediate attention to data privacy and regulatory compliance, particularly as LLMs move into diverse linguistic contexts.
Ensuring the responsible development of LLMs requires robust methods for detecting and filtering sensitive personal information to maintain user trust, comply with international privacy laws, and prevent reputation damage or legal liabilities for developers and deployers.
Research into privacy-preserving LLM development is expanding beyond English, addressing unique linguistic and regulatory challenges in other major languages, pushing towards more globally compliant and ethically sound AI systems.
- · AI developers focused on ethical AI and privacy
- · Japanese AI companies and research institutions
- · Users and consumers concerned about data privacy
- · Regulatory bodies focused on data protection
- · AI models trained on unfiltered, high-risk data
- · Organizations neglecting data privacy in AI development
- · Companies facing legal action due to data breaches
Increased development and adoption of privacy-enhancing technologies within NLP workflows for non-English languages.
Heightened competition for 'clean' and ethically sourced datasets, potentially leading to new data governance standards.
Differentiated market advantage for AI models and platforms that demonstrably prioritize and achieve robust data privacy and regulatory compliance across diverse linguistic regions.
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