Critical Trade-offs in Privacy Compliant ML

Core Concept
Privacy compliance creates fundamental tensions with ML optimization. Understanding these trade-offs helps make informed decisions about compliance investment versus model degradation.
DATA VOLUME VS MINIMIZATION
ML models improve with more data, but minimization requires collecting only what you need. A recommendation system on 2 years of history outperforms 90-day retention, but regulations may require 90-day limits. Quantify: if 2 years yields 85% precision and 90 days yields 78%, is 7% worth the compliance risk?
MODEL QUALITY VS DELETION
When users request deletion, data is already baked into weights. Options: retrain from scratch (expensive, 24-48 hours), use machine unlearning (5-15% accuracy impact), or accept influence until next scheduled retrain.
💡 Key Insight: Most companies batch deletion requests and retrain weekly/monthly. Immediate unlearning is technically possible but costly. Document your approach for regulators.
LATENCY VS CONSENT CHECKS
Real-time consent verification adds 15-30ms per inference. A feed returning in 50ms becomes 65-80ms. At millions of requests, this adds significant cost. Solutions: cache consent decisions (risk: stale), batch lookups, or accept degraded latency.
FEATURE RICHNESS VS AUDIT COMPLEXITY
Rich features improve models but require tracking lineage for every transformation. Simpler features are easier to audit and delete but may underperform by 10-20%.
⚠️ Key Trade-off: No free lunch. Every compliance investment has cost—latency, accuracy, engineering time. Quantify trade-offs and make explicit decisions.

💡 Key Takeaways

✓Data minimization conflicts with ML need for large training sets—quantify accuracy trade-offs

✓Deletion requires retraining or unlearning; most batch deletions and retrain periodically

✓Consent verification adds 15-30ms latency; cache decisions to reduce impact

📌 Interview Tips

1Quantify: if 2 years yields 85% precision vs 78% with 90 days, is 7% worth compliance risk?

2Discuss batched retraining for deletion—immediate unlearning is costly

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