The technique that adds calibrated noise to model outputs to prevent membership inference is called ________ Privacy.
- Differential
- MITRE ATT&CK
- Man
- Beam / Nucleus
Answer: Differential
Differential Privacy mathematically guarantees that model outputs don't reveal whether specific individuals were in training data. Critical for privacy-preserving ML deployment.