Publication:
Modular Sumcheck Proofs With Applications to Machine Learning and Image Processing

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.contributor.authorBalbás, David
dc.contributor.authorFiore, Dario
dc.contributor.authorGonzález Vasco, María Isabel
dc.contributor.authorRobissout, Damien
dc.contributor.authorSoriente, Claudio
dc.date.accessioned2023-12-19T08:35:14Z
dc.date.available2023-12-19T08:35:14Z
dc.date.issued2023-11-21
dc.description.abstractCryptographic 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 5x faster proving time and 10x shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.en
dc.format.extent15es
dc.identifier.bibliographicCitationBalbás, D., Fiore, D., González Vasco, M. I., Robissout, D., Soriente, C. (26-30 november 2023). Modular Sumcheck Proofs With Applications to Machine Learning and Image Processing [proceedings]. CCS '23: 2023 ACM SIGSAC Conference on Computer and Communications Security, Copenhagen, Denmarcken
dc.identifier.doihttps://doi.org/10.1145/3576915.3623160
dc.identifier.isbn979-8-4007-0050-7
dc.identifier.publicationfirstpage1437es
dc.identifier.publicationlastpage1451es
dc.identifier.publicationtitleCCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Securityen
dc.identifier.urihttps://hdl.handle.net/10016/39111
dc.identifier.uxxiCC/0000034588
dc.language.isoenges
dc.publisherAssociation for Computing Machinery (ACM)es
dc.relation.eventdate2023-11-26es
dc.relation.eventplaceDinamarcaes
dc.relation.eventtitleCCS '23: ACM SIGSAC Conference on Computer and Communications Securityen
dc.rights© 2023 Copyright held by the owner/author(s).en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaElectrónicaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaMatemáticases
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherProof systemsen
dc.subject.otherVerifiable computationen
dc.subject.otherZero-knowledge proofsen
dc.subject.otherMachine learningen
dc.subject.otherConvolutional neural networksen
dc.subject.otherImage processingen
dc.titleModular Sumcheck Proofs With Applications to Machine Learning and Image Processingen
dc.typeconference proceedings*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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