AI Data Privacy

Privacy-preserving AI techniques enable ML on sensitive data while protecting individual privacy. Meet GDPR, HIPAA, CCPA requirements with differential privacy, federated learning, and encryption - maintaining 90-98% of non-private model accuracy.

Privacy Techniques

1. Differential Privacy

• Add calibrated noise to data/models
• Mathematical privacy guarantee (ε-privacy)
• Used by Apple, Google, Microsoft
• 1-5% accuracy trade-off for strong privacy

2. Federated Learning

• Train on distributed data without centralizing
• Data never leaves source (hospitals, devices)
• Only model updates shared
• 90-98% of centralized accuracy

3. Homomorphic Encryption

• Compute on encrypted data
• Never decrypt during processing
• 100% privacy, but slow (100-1000x overhead)
• Emerging technology

4. Secure Multi-Party Computation (MPC)

• Multiple parties compute jointly without revealing data
• Split data/computation across parties
• Used in secure analytics

Regulatory Compliance

• GDPR: Right to explanation, data minimization
• HIPAA: Healthcare data protection (US)
• CCPA: California consumer privacy
• EU AI Act: High-risk AI systems regulation

Implementation

Differential Privacy

• Libraries: TensorFlow Privacy, Opacus (PyTorch)
• ε (epsilon): Privacy budget (smaller = more private)
• Trade-off: Privacy vs accuracy

Federated Learning

• Frameworks: TensorFlow Federated, PySyft, Flower
• Process: Distributed training, central aggregation
• Applications: Healthcare, finance, mobile AI

Results

• Differential Privacy (ε=1.0): 2-5% accuracy drop
• Federated Learning: 90-98% of centralized accuracy
• Full compliance with GDPR/HIPAA
• No centralized data breaches