住民票コード
Detects 住民票コード patterns. This pattern is based on a Microsoft Purview built-in sensitive information type. Users already running Purview may prefer to enable the built-in SIT directly, or use this version as a starting point for customisation.
- Type
- regex
- Engine
- universal
- Confidence
- medium
- Confidence justification
- Medium confidence: pattern has structural constraints but corroborative keywords are recommended to reduce false positive rates. Context label evidence plus explicit template/example exclusion improves precision for high-risk identifiers. Added context gating and exclusion rules improve precision and reduce incidental matches.
- Detection quality
- Verified
- Jurisdictions
- jp
- Regulations
- APPI
- Frameworks
- ISO 27001, ISO 27701
- Data categories
- pii, government-id
- Scope
- narrow
- Risk rating
- 9
- Platform compatibility
- Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible
Pattern
\b\d{11}\bCorroborative evidence keywords
住民票コード, resident registration, jūminhyō, ID number, identification, ID card, license, permit, registration, certificate, field, column, row, entry, record, value, form, register, database, extract (+19 more)
Proximity: 300 characters
Should match
12345678901— Eleven-digit resident registration number98765432109— Another eleven-digit number45678901234— Valid format registration number
Should not match
1234567890— Too few digits (10)123456789012— Too many digits (12)sample template placeholder number 123456789— Template/sample context should be excluded even when numeric-like values appeartemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- Eleven-digit numeric sequences may match phone numbers or other administrative identifiers. Mitigation: Require corroborative evidence keywords such as "住民票コード" or "resident registration" within the proximity window.
- In multiple languages, similar terminology used in formal or administrative contexts (education, professional documentation) that does not constitute sensitive data collection. Mitigation: Layer with additional contextual signals such as structured identifiers, form fields, or database column headers to distinguish sensitive records from general references.