Privacy-Preserving Machine Learning
Definition
Privacy-preserving ML limits privacy risks when training and applying models.
Methods intended to limit privacy risks during training or use of ML models.
Simply explained
Models should be able to learn from data without unnecessarily revealing information about individual persons.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
Multiple locations train using federated learning with additional differential privacy.
Limits and pitfalls
- Consider only data locality as protection or ignore metadata, model leaks, and attacks.
Origin & history
PPML combines differential privacy, cryptography, distributed learning, and security research.
Use in mid-sized companies
- Possible example: Multiple locations train using federated learning with additional differential privacy.
Frequently asked questions
What does privacy-preserving machine learning mean?
Methods intended to limit privacy risks during training or deployment of ML models. What mistakes should be avoided? Consider only data locality as protection or ignore metadata, model leaks, and attacks.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: