PsvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy

Fan, Y; Xu, B; Zhang, L; Tan, G; Yu, S; Li, KC; Zomaya, A

PsvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy

Fan, Y Xu, B Zhang, L Tan, G Yu, S

Li, KC Zomaya, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cloud Computing, 2024, 12, (2), pp. 359-369
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Fan, Y
dc.contributor.author	Xu, B
dc.contributor.author	Zhang, L
dc.contributor.author	Tan, G
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Li, KC
dc.contributor.author	Zomaya, A
dc.date.accessioned	2024-08-07T04:50:38Z
dc.date.available	2024-08-07T04:50:38Z
dc.date.issued	2024-04-01
dc.identifier.citation	IEEE Transactions on Cloud Computing, 2024, 12, (2), pp. 359-369
dc.identifier.issn	2168-7161
dc.identifier.issn	2168-7161
dc.identifier.uri	http://hdl.handle.net/10453/180238
dc.description.abstract	Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem, when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. In terms of proof time and storage, experiments show that psvCNN is practical and efficient. psvCNN generates a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times quicker than the latest zk-SNARK-based non-interactive method vCNN and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/LP190100676
dc.relation.ispartof	IEEE Transactions on Cloud Computing
dc.relation.isbasedon	10.1109/TCC.2024.3350233
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	4606 Distributed computing and systems software
dc.title	PsvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:50:35Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	2

Abstract:

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem, when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. In terms of proof time and storage, experiments show that psvCNN is practical and efficient. psvCNN generates a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times quicker than the latest zk-SNARK-based non-interactive method vCNN and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.

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http://hdl.handle.net/10453/180238