How do you handle PII in ML pipelines?
hardAnswer
- (1) Minimize collection: only fields needed.
- (2) Pseudonymization: hash identifiers.
- (3) Differential privacy: add calibrated noise (DP-SGD for training).
- (4) Federated learning: train on-device, share gradients (not data).
- (5) Access control + audit logs.
- (6) Encryption at rest + in transit.
- (7) Right-to-forget: pipelines that remove user data + retrain.
- (8) Model inversion / membership inference attacks: test with DP + regularization.
- Regulations: GDPR, CCPA, HIPAA (healthcare).
Check yourself — multiple choice
- Random
- Minimize + pseudonymize + DP-SGD + federated + access control + encryption + right-to-forget pipelines + defense against inversion/membership; GDPR/CCPA/HIPAA
- Not important
- Just encrypt
PII in ML: minimize + DP + federated + access + right-to-forget.
#safety#data-quality
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