Privacy-Preserving Learning

The Secure Lattice AI continuously evolves, but never at the expense of privacy. It leverages federated learning (FL) and differential privacy (DP) to ensure that no raw data is ever centralized.

Federated AI Model Training

• Federated Learning Nodes run within enterprise deployments. They train local anomaly models using only local telemetry.

• Model Weights, not Data are shared with the global Lattice AI Core.

• Each update is aggregated using Secure Multi-Party Computation (SMPC), ensuring no participant can reverse-engineer another’s contribution.

• Weight deltas are verified and anchored on-chain for model provenance — guaranteeing auditability of AI evolution.

Differential Privacy

Before weights are transmitted, Gaussian noise is applied to gradients to mask individual contribution — mathematically ensuring privacy with parameters (ε, δ).

Example:

∇w′=∇w+N(0,σ2)∇w' = ∇w + N(0, σ²) ∇w′=∇w+N(0,σ2)

Typical configuration: ε = 1.0 (strong privacy) δ = 10⁻⁵ σ = 1.5

Example Use Case

A healthcare provider trains local models to detect PHI exfiltration anomalies. Their AI node learns user patterns (access frequency, query types) and sends anonymized weight deltas to the federation. No PHI or user identifiers ever leave their network, yet global accuracy improves. This approach aligns with HIPAA 164.308(b)(1) and GDPR Recital 26 principles of data minimization.