analytics-privacy-anonymization
Anonymize analytics data to protect player privacy
Datos de origen
- Repositorio
- LgrappaG/Workflows-Agents
- Última actividad en el origen
- 29 de marzo de 2026 a las 21:56
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 2
- Forks
- 0
Opciones de instalación
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- analytics-privacy-anonymization
- description
- Anonymize analytics data to protect player privacy
- risk
- high
- source
- workspace
- date_added
- 2026-03-24
- usage
- GDPR compliance automation (automatically redact PII on event entry), consent-driven analytics (block metrics from EU users without marketing consent), right-to-be-forgotten fulfillment (revoke user's hashed ID from all fact tables), cross-border data transfer compliance (anonymize before regional transmission)
- avoid
- Hashing PII without salt enabling rainbow-table lookup, storing consent as blob without granularity (can't revoke 1 category), logging raw PII in audit trails (defeats anonymization), shipping raw email/IP in events, deleting consent records at GDPR request without keeping revocation proof, assuming hashing alone prevents re-identification via statistical methods
- mandates
- PII detection accuracy >=99%, GDPR compliance audit trail, consent tracking per user per data category, re-identification resistance >=99% via hashing + salting
- response
- Deploy PII detection: regex scan (email, phone, SSN, credit card patterns) +, Maintain consent registry: {user_id, categories: [analytics, marketing, crash_reports], granted_timestamp, revoke_timestamp}; apply consent, Hash personally identifiable fields (user_id→sha256(user_id+salt)) downstream in aggregation; salt rotates annually; log, Validate anonymization: weekly re-identification attack simulation (reverse hash lookups, statistical linkage to
# Analytics Privacy Anonymization
Anonymize analytics data to protect player privacy
## Risk Level
**HIGH**
## Core Rules
- Implement properly
- Test thoroughly
- Validate results
## Response Pattern
1. Design appropriate approach
2. Implement solution
3. Test edge cases
4. Validate quality
## Usage Contexts
- Privacy protection
- Development workflows
## What NOT to Do
- Incomplete anonymization
- Incomplete testing
- Deploy without validation
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