Paper 2023/1342

Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing

David Balbás, IMDEA Software Institute, Universidad Politécnica de Madrid
Dario Fiore, IMDEA Software Institute
Maria Isabel González Vasco, Universidad Carlos III de Madrid
Damien Robissout, IMDEA Software Institute
Claudio Soriente, NEC Laboratories Europe

Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications - e.g., machine learning or image processing - are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline. In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline. We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to $5 \times$ faster proving time and $10 \times$ shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. Minor revision. ACM CCS 2023
Proof SystemsVerifiable ComputationZero-Knowledge ProofsMachine LearningImage Processing.
Contact author(s)
david balbas @ imdea org
dario fiore @ imdea org
mariaisabel vasco @ urjc es
damien robissout @ imdea org
claudio soriente @ neclab eu
2024-04-18: revised
2023-09-08: received
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Creative Commons Attribution


      author = {David Balbás and Dario Fiore and Maria Isabel González Vasco and Damien Robissout and Claudio Soriente},
      title = {Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1342},
      year = {2023},
      doi = {10.1145/3576915.3623160},
      note = {\url{}},
      url = {}
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