Zero-Knowledge Machine Learning (ZKML)
Verifiability Mechanism
Cryptographic technique that generates mathematical proofs that a specific model produced a specific output, without revealing the model weights or input data — enabling trustless verification of AI inference.
ZKML applies zero-knowledge proof systems (zk-SNARKs, zk-STARKs) to neural network inference. The prover runs the model normally, then generates a proof that the computation was performed correctly. The verifier can check this proof in milliseconds without re-running the model. Implementation: the neural network's forward pass is compiled into an arithmetic circuit, then proven using a ZK proof system. Libraries: EZKL (converts ONNX models to ZK circuits), Modulus Labs (optimized transformer proving), Giza (Cairo-based ML proofs). Current limitation: proof generation is 100-10,000x slower than inference, limiting practical model size.
Why Does This Exist?
Provides cryptographic proof that a specific model performed a specific inference — the foundational primitive for trustless AI verification
If inference is verifiable, claims can be traced to specific model versions and inputs, grounding epistemic provenance in cryptographic rather than social trust
ZK proofs can cryptographically verify that unlearning was performed — prove that a post-edit model no longer produces outputs dependent on deleted data, satisfying the verifiability requirement of GDPR Article 17 compliance