Time-Optimal Interactive Proofs for Circuit Evaluation
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Abstract (arXiv)
Recently, researchers have been working toward the development of practical general-purpose protocols for verifiable computation. These protocols enable a computationally weak verifier to offload computations to a powerful but untrusted prover, while providing the verifier with a guarantee that the prover performed the computations correctly. Despite substantial progress, existing implementations are not yet practical. The main bottleneck is typically the extra effort required by the prover to return an answer with a guarantee of correctness, compared to returning an answer with no guarantee. We describe a refinement of a powerful interactive proof protocol originally due to Goldwasser, Kalai, and Rothblum. Cormode, Mitzenmacher, and Thaler show how to implement the prover in this protocol in time O(S log S), where S is the size of an arithmetic circuit computing the function of interest. Our refinements apply to circuits whose wiring pattern is sufficiently "regular"; for these circuits, we bring the runtime of the prover down to O(S). That is, our prover can evaluate the circuit with a guarantee of correctness, with only a constant-factor blowup in work compared to evaluating the circuit with no guarantee. We argue that our refinements capture a large class of circuits, and prove some theorems formalizing this. Experimentally, our refinements yield a 200x speedup for the prover over the implementation of Cormode et al., and our prover is less than 10x slower than a C++ program that simply evaluates the circuit. Along the way, we describe a special-purpose protocol for matrix multiplication that is of interest in its own right. Our final contribution is a protocol targeted at general data parallel computation. Compared to prior work, this protocol can more efficiently verify complicated computations as long as that computation is applied independently to many pieces of data.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2013.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 4; 2025: 1; 2026: 4.
14 papers.
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- 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
- 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofscited by 30
- 2022pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testingcited by 20
- 2023zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Trainingcited by 20
- 2021SoK: Machine Learning Governancecited by 9
- 2024Models That Prove Their Own Correctnesscited by 6
- 2024SVIP: Towards Verifiable Inference of Open-source Large Language Modelscited by 6
- 2025FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledgecited by 5
- 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flowscited by 4
- 2024Blockchain and Artificial Intelligence: Synergies and Conflictscited by 2
- 2026Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificatescited by 1
- 2026Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inferencecited by 0
- 2026Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation9 Sep 2026
- 2026Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees17 Aug 2026
- 2026Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference23 Jul 2026
- 2026Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates6 Apr 2026
- 2025FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge12 May 2025
- 2024SVIP: Towards Verifiable Inference of Open-source Large Language Models29 Oct 2024
- 2024Models That Prove Their Own Correctness24 May 2024
- 2024Blockchain and Artificial Intelligence: Synergies and Conflicts22 May 2024
- 2024zkLLM: Zero Knowledge Proofs for Large Language Models24 Apr 2024
- 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flows5 Dec 2023
- 2023zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training30 Jul 2023
- 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofs17 Oct 2022
2 more
- 2022pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing23 Jan 2022
- 2021SoK: Machine Learning Governance20 Sep 2021
- 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
- 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofscited by 30
- 2022pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testingcited by 20
- 2023zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Trainingcited by 20
- 2021SoK: Machine Learning Governancecited by 9
- 2024Models That Prove Their Own Correctnesscited by 6
- 2024SVIP: Towards Verifiable Inference of Open-source Large Language Modelscited by 6
- 2025FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledgecited by 5
- 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flowscited by 4
- 2024Blockchain and Artificial Intelligence: Synergies and Conflictscited by 2
- 2026Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificatescited by 1
- 2026Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inferencecited by 0
12 most cited of 14 All 14 (JSON)
- 2026Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation9 Sep 2026
- 2026Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees17 Aug 2026
- 2026Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference23 Jul 2026
- 2026Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates6 Apr 2026
- 2025FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge12 May 2025
- 2024SVIP: Towards Verifiable Inference of Open-source Large Language Models29 Oct 2024
- 2024Models That Prove Their Own Correctness24 May 2024
- 2024Blockchain and Artificial Intelligence: Synergies and Conflicts22 May 2024
- 2024zkLLM: Zero Knowledge Proofs for Large Language Models24 Apr 2024
- 2023All Rivers Run to the Sea: Private Learning with Asymmetric Flows5 Dec 2023
- 2023zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training30 Jul 2023
- 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofs17 Oct 2022
12 newest of 14 All 14 (JSON)