Privacy-Preserving Machine Learning at Production Scale

Data privacy and AI capability seem at odds. Our research demonstrates they can coexist in production systems.

The Privacy Imperative

Regulations like GDPR and CCPA demand:

• Minimizing data collection
• Preventing inference attacks
• Enabling user data deletion
• Providing transparency

Technical Approaches

We combine multiple techniques:

Differential Privacy

• Formal privacy guarantees
• Noise calibration for utility
• Privacy budget management
• Composition theorems

Federated Learning

• Data stays on device
• Model updates are aggregated
• Secure aggregation protocols
• Asynchronous training

Secure Computation

• Multi-party computation
• Homomorphic encryption
• Trusted execution environments

Production Deployment

The 7lineas Privacy Platform serves:

• 50M daily active users
• <10ms latency overhead
• ε = 1.0 privacy guarantee
• 97% of centralized accuracy

Case Studies

Healthcare

• Predicting readmission risk
• Training across 12 hospitals
• No patient data shared
• 15% improvement in outcomes

Finance

• Fraud detection
• Collaborative model training
• Regulatory compliance
• $50M annual savings