Privacy-Preserving Machine Learning
Techniques for training AI models while protecting sensitive data
Overview
Privacy-preserving machine learning enables AI model development without compromising data privacy. It combines cryptographic methods, secure computing, and privacy-enhancing techniques to protect sensitive information throughout the machine learning lifecycle.
Privacy Mechanisms
Cryptographic Methods
Essential techniques: → Homomorphic encryption: Allows computations on encrypted data without decrypting it first, enabling AI models to learn from sensitive data while keeping it protected → Secure enclaves: Protected memory regions that isolate sensitive computations from the rest of the system, providing a safe environment for AI training → Multi-party computation: Enables multiple parties to jointly compute AI models without revealing their private input data to each other → Zero-knowledge proofs: Verifies AI model properties and results without exposing the underlying data or model details → Secure aggregation: Combines model updates from multiple sources while preserving individual privacy, crucial for federated learning
Data Protection
Key approaches:
- Access Controls
- Authentication
- Authorization
- Audit logging
- Privacy Techniques
- Anonymization
- Pseudonymization
- Encryption
Security Architecture
System Components
Critical elements:
- Secure computation
- Data isolation
- Protocol enforcement
- Access management
- Audit mechanisms
Protection Layers
Security measures:
- Encryption protocols
- Secure channels
- Trust boundaries
- Attack prevention
- Recovery systems
Training Methods
Secure Learning
Key techniques:
- Distributed Training
- Federated learning
- Split learning
- Secure aggregation
- Local Processing
- On-device training
- Edge computing
- Private inference
Model Protection
Essential safeguards:
- Model encryption
- Secure updates
- Access controls
- Version tracking
- Integrity checks