GDPR Principles and Compliance Questions

Situation: I was technical lead for an ML team building personalization models that used user behavioral data and needed to be GDPR-compliant.

Process I’d establish:

• Data minimization: require explicit business justification for every raw field; create a "feature approval" flow where features must state purpose, legal basis, retention, and risk.
• Consent & subject rights: integrate a consent-tracking service (stable user IDs with consent flags) and ensure pipelines honor revocation (right to be forgotten) via delete/pseudonymize flows.
• DPIA & risk reviews: mandatory Data Protection Impact Assessments for projects using personal data; periodic privacy reviews.

Tooling and techniques:

• Anonymization/pseudonymization: use tokenization/hashing with salted keys, ARX or custom k-anonymity scripts for datasets used in analysis.
• Differential privacy: apply Google DP library or OpenDP for aggregate statistics and noisy gradients in training when required.
• Synthetic data: use privacy-preserving synthetic generators (SDV) for dev/test environments.
• Libraries: PySyft or TFF for federated learning where central data transfer is undesirable.

Access controls & infra:

• Enforce RBAC via IAM, least privilege for feature store and data lakes; separate dev/prod environments and network isolation (VPC).
• Encrypt data at rest and in transit; use KMS for key rotation.
• Audit logging: immutable logs of data access, feature creation, and model training runs for compliance.

Coaching engineers:

• Teach concrete patterns: never log raw PII, use privacy-safe summary features, check consent flags before joins.
• Enforce privacy through CI: automated checks (linting rules, unit tests that fail if PII fields are used without approval), feature-review checklist in PRs.
• Run workshops: threat modeling, hands-on sessions implementing DP noise, anonymization examples.
• Documentation: require a brief “privacy note” in every feature’s spec documenting data sources, legal basis, transformation, retention, and rollback plan.

Result: These practices create auditable, scalable pipelines that respect user rights while enabling safe ML experimentation.