Privacy in Machine Learning
Protect data in ML.
Techniques
Secure multi-party computation. Homomorphic encryption. Trusted execution.
Frameworks
CrypTen. PySyft. TF Encrypted.
Applications
Private training. Private inference. Secure aggregation.
Key Takeaways
- SMC for private computation
- Homomorphic encryption
- Secure ML frameworks