Requirements & constraints:
- Must honor GDPR/CCPA: right to erasure, rectification, portability, audit.
- Minimal model-quality degradation and acceptable latency for updates.
- Scalable for Apple-scale data, privacy-preserving.
High-level architecture:
- Ingest → Raw Data Lake (encrypted, tenant-tagged) → Data Provenance & Lineage Store → Feature Store (materialized features with origin pointers) → Training Orchestrator → Model Registry & Audit Ledger → Serving.
Key components and behaviors:
- Data lineage & provenance
- Every data record gets immutable metadata: user_id_hash, consent_flags, source, timestamp, dataset_id, and a provenance_id.
- Lineage store (graph DB) links features and model training runs to provenance_ids; used to compute affected models when a record changes.
- Soft deletes + rectification
- Soft delete marks provenance_id as "deleted" and records deletion_time; rectification creates a new record with linkage to original.
- Physical purge happens asynchronously after legal TTL (or immediate if requested): purge scheduler consults lineage to identify derived artifacts.
- Selective retraining
- Use the lineage store to compute which training examples and features included a deleted/rectified record; mark affected model versions.
- For minor impact (small influence score via influence functions or Shapley/value-attribution), apply targeted fine-tuning on affected layers or delta-updates rather than full retrain.
- Maintain shadow retraining jobs to evaluate drift from deletions before promoting.
- Federated retraining & minimization
- For sensitive or high-volume user data, prefer on-device federated learning: local updates, secure aggregation, differential privacy noise, and only send model deltas.
- Combine federated deltas with server-side selective retraining to reduce need to store raw personal data centrally.
- Data valuation & priority
- Score records by influence on model (per-run), recency, and consent; prioritize retraining for high-impact deletions.
- Model auditability & compliance evidence
- Immutable audit ledger (WORM) logs: data ingestion events, consent changes, delete/rectify requests, lineage mappings, training run IDs, model binary hashes, evaluation metrics, and promotion events.
- Model registry stores model artifacts, training code, hyperparameters, dataset snapshot references (not necessarily raw PII), and a list of provenance_ids or dataset-level signatures.
- Provide explainability artifacts (feature importances) per model version to demonstrate why a deletion affected outputs.
- Expose an API for compliance: given user_id_hash, return evidence of removal/rectification (timestamps, models affected, retrain status) and allow portability.
Privacy & security controls:
- Encrypt data at rest and in transit; zero-knowledge hashing/pseudonymization for IDs; key management with least privilege.
- Differential privacy and secure aggregation for federated updates.
- Access controls and approval workflow for any physical purge.
Operational flow for a deletion request:
- User requests deletion → Authenticated request logged in audit ledger.
- Mark provenance_ids as deleted in lineage store (soft delete), block future ingestion/usage.
- Compute affected features/models via lineage graph; compute influence scores.
- If high impact: trigger retrain/fine-tune job (or initiate federated update), run shadow evaluation, then promote new model version; log everything.
- After legal windows and backups accounted, trigger physical purge (scrub storage, update audit logs).
Trade-offs:
- Immediate full retraining ensures strongest compliance but is costly and may hurt availability; selective retraining + influence scoring balances cost and model quality.
- Federated retraining reduces central storage of PII but increases complexity and requires secure aggregation/infrastructure.
Why this works:
- Lineage enables precise identification of affected artifacts so deletions are surgical, reducing unnecessary retrains.
- Soft deletes provide reversible steps for rectification and a clear audit trail.
- Selective retraining and federated methods minimize quality impact and central PII retention.
- Immutable audit ledger + model registry provide verifiable evidence for regulators and users.
