Lab: Build a Privacy-Aware Inference App

What you'll build

Vertical stack context: DOSFIUniversity (research) → DOSFI Academy (this course) → MeshNativeExchange (publishing & monetizing). Students progress from Learning → Building → Publishing → Monetizing. In this intermediate lab you will implement privacy tier enforcement in a real app. The app is a personal health data analyzer that runs inference on user health metrics. Users must be able to choose their privacy tier, and the app must enforce it at the routing layer — rejecting inference tasks that would route to unauthorized nodes. This module provides a comprehensive technical guide to developing privacy-aware inference applications within the DOSFI Mesh-Native ecosystem. Learners will architect local-first data pipelines using the DIU (Distributed Intelligence Unit), implement Routing Policies to enforce Privacy Tiers, and leverage the identity layer for secure MNE (MeshNativeExchange) interactions. By the conclusion, participants will possess the skills to deploy hardened MeshInfer workflows that maintain data sovereignty through deterministic execution.

Skills you'll practice

• Configure DIU for local-first inference execution
• Define Routing Policies that enforce specific Privacy Tiers
• Integrate the Identity Fabric for MNE data stream authorization
• Build a state-machine-driven workflow for MeshInfer
• Validate Bundle Schema integrity in a distributed environment

Before you start

• Basic proficiency in distributed system architectures
• Understanding of Mesh-Native Transport Layer fundamentals
• Experience with JSON-based configuration schemas
• Familiarity with containerized workload management

Lab steps6 steps

1. 1
   Architecting the DIU Data Pipeline
2. 2
   Enforcing Privacy Tiers in Routing Policies
3. 3
   Integrating MeshInfer with Identity Fabric
4. 4
   Validating Inference Integrity with Transport Layer Proofs
5. 5
   Optimization for MeshNativeExchange
6. 6
   Full Deployment and Privacy Audit