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Certified but Private: Scalable Zero-Knowledge Proofs for the Formal Verification of Neural Networks

NeurIPS 2026 · Learning theory and algorithms › Combinatorial and search optimization · approach: Semidefinite relaxations for verification

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Proposes PANDA, a commit-and-prove ZKP system that certifies CROWN-based local robustness and fairness of private neural networks, using a Four-Point Relaxation Gadget to verify activation linear relaxations in polynomial time.

Problem

Proving local robustness of neural networks in zero knowledge (hiding model weights) is infeasible for large networks: prior ZKP robustness/fairness systems (FairProof) enumerate neuron activations exponentially and handle only tiny models.

Approach

Run CROWN outside the ZKP to find linear relaxations of activations, then certify them inside the ZKP via a case-free four-point check (endpoints and two tangency points) using lookup and matrix-arithmetic arguments, yielding polynomial prover time.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: MNIST SafeNLP LunarLander FairProof Adult

Benchmarks: MNIST SafeNLP LunarLander FairProof Adult

Metrics: prove time verify time proof size CROWN time drift (%) number of certified properties

Baselines: FairProof CROWN

Builds on: CROWN FairProof Hyrax LogUp-GKR alpha-CROWN

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