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

Layered Strategy: Combining Anonymization Techniques in Production ML

Definition
A layered anonymization strategy combines multiple techniques—PII removal, k-anonymity, differential privacy—at different pipeline stages for defense-in-depth.
LAYER 1: DATA INGESTION
Remove direct identifiers immediately at collection. Strip names, emails, SSNs before data enters the pipeline. Use tokenization for fields needing longitudinal tracking. This prevents accidental PII exposure in logs, caches, or intermediate storage.
LAYER 2: FEATURE ENGINEERING
Apply k-anonymity to quasi-identifiers before feature computation. Generalize zip codes to regions, ages to ranges. For high-cardinality categoricals, apply suppression or grouping to ensure minimum group sizes.
💡 Key Insight: Derived features can leak more than raw data. Aggregations like "average transaction value" are safer than individual values. Feature design must consider privacy implications.
LAYER 3: MODEL TRAINING
For sensitive applications, add differential privacy using DP-SGD (gradient clipping + noise). Prevents model from memorizing training examples. Set epsilon based on sensitivity: ε=1-3 for healthcare, ε=5-10 for behavioral data.
LAYER 4: MODEL OUTPUT
Apply output perturbation for aggregate queries. Cap prediction confidence to prevent high-certainty outputs that leak information. Monitor for memorization through carefully crafted test queries.
⚠️ Key Trade-off: Each layer adds privacy but compounds utility loss. A system with 5% loss per layer sees ~20% total loss. Balance layers based on threat model.

💡 Key Takeaways

✓Layered approach: PII removal at ingestion, k-anonymity in features, differential privacy in training

✓Each layer compounds utility loss—balance based on threat model

✓Derived features can leak more than raw data; feature design must consider privacy

📌 Interview Tips

1Remove identifiers at ingestion, apply k-anonymity before features, add DP during training if needed

2Set epsilon based on sensitivity: 1-3 for healthcare, 5-10 for behavioral data

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