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

Production Implementation: Multi-Tier PII Detection Pipeline

Definition
A Multi-Tier PII Detection Pipeline combines rule-based regex, NER models, and statistical methods to identify PII across data sources before ML training.
TIER 1: RULE-BASED DETECTION
Deterministic regex patterns for structured PII: emails, phone numbers, credit cards, SSNs, IPs. Fast (microseconds per record), high precision for well-formatted data, but misses variations and cannot detect free-text PII like names in sentences.
TIER 2: NER-BASED DETECTION
Named entity recognition (fine-tuned BERT or spaCy) detects PII in unstructured text: names, addresses, organizations. Set confidence thresholds based on risk—high-sensitivity data uses lower thresholds (more false positives, fewer misses).
💡 Key Insight: NER requires domain-specific training. A medical NER may miss financial PII patterns. Build specialized models per domain or ensemble multiple models.
TIER 3: STATISTICAL DETECTION
Detects quasi-identifiers by analyzing column uniqueness and correlation. High-cardinality columns are flagged as potential identifiers. Correlation analysis identifies field combinations enabling re-identification even when individual fields appear safe.
PIPELINE ARCHITECTURE
Run tiers sequentially: regex first (fast), NER second (broader coverage), statistical last (batch). Log detected PII with confidence for human review. Build feedback loops where reviewers correct mistakes to improve accuracy.
⚠️ Key Trade-off: Aggressive detection (lower thresholds) catches more PII but increases false positives, requiring more review and potentially anonymizing useful non-sensitive data.

💡 Key Takeaways

✓Multi-tier detection combines regex (fast, structured), NER (unstructured text), and statistical analysis (quasi-identifiers)

✓NER models require domain-specific training—medical NER may miss financial PII patterns

✓Statistical methods detect quasi-identifiers through uniqueness and correlation analysis

📌 Interview Tips

1Run tiers sequentially: regex first for speed, NER for coverage, statistical for quasi-identifier discovery

2Build feedback loops where human reviewers correct false positives to improve detection

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