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Privacy & Fairness in ML • Data Anonymization (PII Removal, k-anonymity)Hard⏱️ ~3 min

Failure Modes: Attacks and Operational Risks in Anonymization

Definition
Anonymization failure modes occur when de-identified data can be re-linked to individuals through linkage attacks, inference attacks, or operational errors.
LINKAGE ATTACKS
Attackers combine anonymized data with external datasets to re-identify individuals. Even with direct identifiers removed, quasi-identifiers (zip + birth date + gender) can match public records. Risk increases over time as more datasets become publicly available.
INFERENCE ATTACKS
Statistical inference reveals sensitive attributes without direct re-identification. If 95% of people in an anonymized group share a disease, attackers infer that attribute with high confidence. ML models may also leak information through membership inference.
💡 Key Insight: K-anonymity protects identity but fails against inference. Use l-diversity (diverse sensitive attributes per group) or t-closeness (distributions match population) for stronger protection.
OPERATIONAL FAILURES
Common risks: incomplete PII detection leaving identifiers in free-text or metadata, version mismatches where non-anonymized data persists in backups, logging systems capturing raw PII. Audit trails must themselves be anonymized.
DEFENSE STRATEGIES
Test with simulated re-identification attacks before release. Use differential privacy for quantifiable guarantees. Implement data minimization. Monitor for emerging auxiliary datasets enabling future attacks.
⚠️ Key Trade-off: Defending sophisticated attacks requires stronger anonymization that degrades utility. Balance attack surface against accuracy requirements.

💡 Key Takeaways

✓Linkage attacks combine anonymized data with external datasets using quasi-identifiers

✓Inference attacks reveal sensitive attributes through statistics without direct re-identification

✓Operational failures include incomplete PII detection, version mismatches, and logging raw data

📌 Interview Tips

1Test anonymized datasets with simulated re-identification attacks before release

2Complement k-anonymity with l-diversity or t-closeness for inference protection

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