84 lines
2.6 KiB
Markdown
84 lines
2.6 KiB
Markdown
---
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name: japanese-pii-redactor
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description: Redact, anonymize, and de-identify personal information in Japanese-language or mixed-language text and tabular data while preserving analytical usefulness. Use this whenever users ask for PII redaction, PII scrub, de-identification, 個人情報匿名化, 匿名加工, 仮名化, 秘匿化, or マスキング; use it for executing anonymization rules, not for legal interpretation or general writing polish.
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category: Compliance & Security
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---
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# Japanese PII Redactor
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## Overview
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Detect and redact personal information in Japanese-language or mixed-language content for safer sharing and analysis.
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Common targets:
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- Person names
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- Phone numbers
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- Email addresses
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- Home/work addresses
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- Account/member/employee identifiers
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- Free-text notes containing identifiable details
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## Triggering Cues
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Use this skill when user messages include:
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- Chinese cues: 脱敏、匿名化、个人信息、隐私遮蔽、PII处理、数据清洗
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- Japanese cues: 個人情報、匿名化、マスキング、伏字、漏えい対策、PII
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- English cues: redact PII, anonymize Japanese data, privacy masking
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## Input Requirements
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Ask for or infer:
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1. Source text/table
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2. Target output format (text/table/json)
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3. Redaction strength (light/standard/strict)
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4. Whether reversible pseudonyms are needed
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## Output Format
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Always output:
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1. **Redacted Data**
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2. **Redaction Rules Applied**
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3. **Fields Preserved vs Masked**
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4. **Residual Risk Notes**
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For rules section, use this schema:
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| Field Type | Detection Pattern | Redaction Method | Example |
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|------------|-------------------|------------------|---------|
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## Workflow
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1. Detect direct identifiers first (email/phone/account IDs).
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2. Detect contextual identifiers (address/detail combinations).
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3. Apply consistent masking policy across the dataset.
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4. Keep analytical utility while minimizing re-identification risk.
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5. Report what was masked and why.
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## Examples
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### Example 1
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Input:
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- 日文客服对话日志，需要共享给外部分析团队。
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Output style:
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- Replace identifiers with neutral tokens
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- Preserve issue semantics and timeline
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### Example 2
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Input:
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- 员工名单（姓名、邮箱、电话、住址、员工编号）。
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Output style:
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- Table output with masked fields and preserved non-sensitive columns
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- Explicit rule list for audit traceability
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## Guidelines
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- Prefer consistency: same entity should map to same token within one output.
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- Never expose raw originals in final output.
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- Mark uncertain detections as "Needs Manual Review".
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- State that redaction reduces risk but does not guarantee zero re-identification risk.
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