Privacy & Fairness in ML • Regulatory Compliance (GDPR, CCPA)Medium⏱️ ~2 min
Critical Trade-offs in Privacy Compliant ML
Every privacy enhancement in ML systems comes with real costs. The privacy utility trade off is fundamental. Differential privacy and aggressive sampling reduce reidentification risk but can reduce model accuracy by 1 to 5 percentage points depending on epsilon and dataset size. For high stakes use cases like credit decisions, privacy noise must be carefully budgeted or replaced with strict access controls and on device processing.
Centralization versus federation presents another critical decision. A centralized data lake simplifies lineage and DSAR propagation, making it easier to track and delete data. However, it concentrates risk and increases the blast radius of breaches. Federated learning or on device computation reduces central exposure but increases client complexity and update latency, and can bias models toward heavy users who generate more training data.
Real time gating versus batch enforcement affects both latency and compliance guarantees. Checking consent and purpose at inference time adds 1 to 5 milliseconds per call if cached, up to 20 milliseconds on cache misses. Batch enforcement in feature materialization reduces runtime overhead but risks serving stale policy decisions for minutes to hours. For immediate unlearning versus periodic retraining, continuous unlearning works for linear or tree models with delta updates within hours, but deep models often require full or partial retraining taking 6 to 48 hours and significant GPU cost.
💡 Key Takeaways
•Differential privacy can reduce model accuracy by 1 to 5 percentage points, requiring careful epsilon tuning for high stakes decisions like credit scoring
•Real time consent checks add 1 to 5 milliseconds per inference call when cached, up to 20 milliseconds on cache misses versus batch enforcement
•Continuous unlearning works for linear and tree models within hours, but deep models need 6 to 48 hours full retraining with significant GPU cost
•Centralized data lakes simplify DSAR propagation but concentrate breach risk, while federated learning reduces exposure but can bias toward heavy users
•Broad data collection improves model power but increases DSAR cost and breach impact, while minimization reduces risk but slows feature discovery
📌 Examples
A fraud detection model using differential privacy with epsilon of 1.0 saw 2 percent accuracy drop but reduced reidentification risk by 90 percent
Federated learning for mobile keyboard predictions reduced central data storage by 100 percent but increased model update latency from hours to days
Real time consent gating at Netflix adds 3 milliseconds p50 latency but ensures zero stale policy decisions versus batch enforcement with up to 2 hour lag