NOVA for Governments: Confidential Document Vaults Before TEE Review

Upload sensitive legal docs to NOVA; TEE processes (e.g., AI contract analysis) without visibility; revoke access post-review—complements Phala's privacy for e-discovery.

Use Case

In Law (e.g., Phala's success story: https://phala.com/posts/confidential-ai-for-law-firms).

How NOVA Complements Private ML SDK

Phala focuses on runtime privacy ("no storage, no logs"); NOVA adds decentralized persistence: encrypt/upload via composite_upload, retrieve into TEE, output back. Focus: Data pipeline security without reinventing Phala's compute. NOVA handles pre-enclave sourcing (group auth) and post-enclave auditing (on-chain hashes/CIDs), extending Phala's quotes for full-lifecycle verifiability.

Running the Demos

• Set .env (NEAR_PRIVATE_KEY, etc.).

• Rust: cargo run --bin tee_federated_learning.

• JS: ts-node demos/tee_federated_learning.ts.

Expected output:

Primary Account ID (Hospital A): <hospital_A.testnet>
Secondary Account ID (Hospital B): <hospital_B.testnet>
Hospital A uploaded to NOVA: CID QmW9oCqbrdMF8cKuYd14cwT3SszMWXRnaJ4aTZXKG4b1QA
Waiting for IPFS pin to propagate...
Hospital B output stored: CID QmSxuYEMhJpCm2wxC9zRQrbvaJN2irGtNdENCxRzs6J39C
Waiting for IPFS pin to propagate...
Hospital A final output stored: CID QmP5TiZYHByf1DDtXxVe5KjHj8r5Sp2E2MzLCMAxfH3Job