Privacy & Fairness in ML • Regulatory Compliance (GDPR, CCPA)Hard⏱️ ~2 min
Runtime Privacy Controls and Audit Evidence
Runtime privacy controls enforce compliance at inference time, the moment when models make decisions about real users. Every inference request should carry context about purpose and jurisdiction. The inference service queries the policy engine to determine which features are allowed and whether automated decision making requires human review. This check happens on every request, making latency critical: well designed consent stores deliver under 10 milliseconds at p99 through aggressive caching and geographic distribution.
Apply privacy preserving ML techniques at runtime. Use pseudonymization for operational handling and encryption in transit and at rest. Apply differential privacy for telemetry and aggregation, enforcing a privacy budget per user per day and minimum cohort sizes of 100 or more for reporting. For personalization features, consider on device computation and local inference when feasible to reduce server side personal data exposure. This is how Apple's keyboard predictions and photo search work without sending raw data to servers.
Audit evidence is your defense in regulatory investigations. Log every decision path with immutable audit records including policy decisions, feature usage, and model version. Store these logs for 12 to 24 months with strict access controls and tamper evidence. Sample and verify DSAR completeness by simulating synthetic users across the system and reconciling expected versus actual data. Monitor data egress, unusual access patterns, and joins that could leak personal data. Establish breach detection with clear triage to meet the GDPR 72 hour reporting clock. Amazon and Microsoft maintain centralized audit systems that can reconstruct any decision path months later during investigations.
💡 Key Takeaways
•Inference services check policy on every request, requiring consent stores under 10 milliseconds p99 latency through aggressive caching
•Apply differential privacy at runtime with privacy budget per user per day and minimum cohort sizes of 100 or more for aggregate reporting
•On device features and local inference reduce server side personal data exposure, used by Apple for keyboard predictions and photo search
•Immutable audit logs must capture policy decisions, feature usage, and model version for 12 to 24 months with strict access controls
•Synthetic user simulation verifies DSAR completeness by tracking expected versus actual data deletion across all systems
•Breach detection must meet GDPR 72 hour reporting clock, requiring automated monitoring of data egress and unusual access patterns
📌 Examples
Apple's on device photo search and keyboard predictions use local inference, avoiding centralized personal data storage while providing personalization
Amazon audit systems can reconstruct any decision path months later, logging feature usage, model version, and policy decisions for regulatory investigations
A fintech company uses differential privacy with epsilon 1.0 for transaction aggregates, enforcing 100 user minimum cohort size to prevent reidentification