Zero-Knowledge Proofs for AI: Proving Predictions Without Revealing Models

You want to verify that an AI company's model made a specific prediction. But they won't share their proprietary weights. Zero-knowledge proofs solve this: prove computation correctness without revealing the computation itself.

🤔 Why ZK Proofs for AI?

Several compelling use cases are driving ZK-ML adoption:

• Model Auditing: Prove your model is unbiased without revealing it
• Regulatory Compliance: Demonstrate FDA-approved model was used for diagnosis
• AI Marketplaces: Let users verify predictions before paying
• Decentralized AI: Trustless inference on blockchain
• IP Protection: Prove model quality without exposing weights

🔧 How ZK-SNARKs Work (Simplified)

1. Arithmetization

Convert neural network computation into arithmetic constraints:

# ReLU as constraints
# y = max(0, x)
# Becomes: y * (y - x) = 0 AND y >= 0 AND y >= x

2. Polynomial Commitment

Encode constraints as polynomials. Use cryptographic commitments (like KZG) to bind prover to specific values without revealing them.

3. Interactive → Non-Interactive (Fiat-Shamir)

Replace verifier challenges with hash function, creating a proof anyone can verify.

⚡ The Scalability Challenge

Proving neural network execution is expensive:

Active research areas:

• Lookup tables: Replace expensive operations (exp, division)
• Folding schemes: Nova, SuperNova for recursive proofs
• Hardware acceleration: GPU/FPGA provers
• Model quantization: Prove quantized models (faster)

🛠️ Tools & Frameworks

EZKL

Convert PyTorch/ONNX models to ZK circuits:

# Export model to ONNX
torch.onnx.export(model, dummy_input, "model.onnx")

# Generate ZK circuit
ezkl gen-settings -M model.onnx
ezkl compile-circuit -M model.onnx -S settings.json

# Create proof
ezkl prove -M model.onnx --witness input.json

zkML by Modulus Labs

Optimized for transformer models, focuses on attention mechanisms.

Risc Zero

General-purpose zkVM—run any code and prove execution.

🌐 Real-World Applications

Worldcoin Iris Recognition

Uses ZK proofs to verify iris scan matches registered identity without storing biometric data centrally.

zkLLM (Research)

Proving language model outputs for verifiable AI-generated content attribution.

DeFi AI Oracles

Prove that price predictions came from specific models, enabling trustless AI in smart contracts.

🔮 The Future: Verifiable AI

Imagine a world where:

• Every AI decision includes a proof of correct execution
• Model auditors verify compliance without accessing weights
• AI-as-a-Service providers prove they used the advertised model
• Decentralized AI markets with trustless verification

📚 Further Reading

• EZKL Documentation: "Zero-Knowledge Machine Learning"
• Kang et al. (2022). "Scaling up Trustless DNN Inference with Zero-Knowledge Proofs"
• Ghodsi et al. (2023). "zkML: Efficient Zero-Knowledge Proofs for ML Inference"