ZIP-0423: Privacy-Preserving AI Training (FHE)

Abstract

This proposal specifies the application of Fully Homomorphic Encryption (FHE) to AI model training and inference, enabling computation on encrypted data without ever decrypting it. For conservation applications, this means sensitive wildlife data (endangered species locations, anti-poaching patrol routes, genetic samples) can be used to improve AI models while remaining encrypted throughout the entire pipeline -- from data collection through training to inference.

Motivation

Conservation data sensitivity requires privacy guarantees beyond differential privacy (ZIP-0410):

1. Endangered species locations: GPS coordinates of critically endangered species (< 100 individuals remaining) are classified intelligence. Exposure enables poaching.
2. Anti-poaching routes: Patrol patterns, if exposed, allow poachers to evade detection.
3. Genetic data: DNA samples from endangered species have black-market value.
4. Indigenous knowledge: Traditional ecological knowledge shared under cultural protocols that prohibit public disclosure.

FHE provides the strongest possible privacy guarantee: the data is never decrypted during computation, so even a compromised training node cannot access raw data.

Specification

FHE Scheme

• Scheme: CKKS (Cheon-Kim-Kim-Song) for approximate arithmetic on encrypted floating-point data
• Security level: 128-bit post-quantum security
• Bootstrapping: Programmable bootstrapping for arbitrary circuit depth

Encrypted Training Pipeline

Data Provider                  Training Node
(conservation org)             (untrusted compute)
      │                              │
      │ Encrypt(data, pk)            │
      ├─────────────────────────────>│
      │                              │ FHE_Train(enc_data, enc_model)
      │                              │ (all operations on ciphertext)
      │         enc_gradient         │
      │<─────────────────────────────┤
      │                              │
      │ Decrypt(enc_gradient, sk)    │
      │ Apply to local model         │

Performance

Operation	FHE Overhead	Throughput
Forward pass (7B model)	1000x	1 sample/sec
Gradient computation	500x	2 samples/sec
Inference (1.5B model)	100x	10 samples/sec

Hybrid Approach

For practical deployment, combine FHE with trusted execution environments (TEEs):

• Sensitive data operations: FHE (maximum privacy)
• Non-sensitive computations: TEE (good privacy, better performance)
• Router decides per-operation based on data sensitivity classification

Research Papers

• zoo-fhe -- FHE for privacy-preserving computation (2024)
• zoo-fhe-ai -- FHE specifically for AI inference (2024)
• zen-privacy-federated -- Privacy-preserving federated training for Zen

Implementation

• hanzo/candle: Rust ML framework with FHE operator support
• hanzo/node: Blockchain node with FHE-enabled compute
• zoo/contracts: Smart contracts for FHE key management

Timeline

• Originated: October 2024 (FHE for AI research)
• Research: zoo-fhe and zoo-fhe-ai published Q4 2024
• Implementation: FHE inference pipeline in Hanzo Candle 2025