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Anonymize analytics data to protect player privacy
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Based on SOC occupation classification
Track and manage player consent for data usage
Design and implement advanced system architectures
Leverage custom attributes for metadata-driven programming
Use IL emit for low-level code generation
Perform advanced memory profiling and leak detection
Implement metaprogramming for code generation and manipulation
| 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 |
Anonymize analytics data to protect player privacy
HIGH
