psvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy

Yongkai Fan, Binyuan Xu, Linlin Zhang, Gang Tan, Shui Yu, Kuan Ching Li, Albert Zomaya

Research output: Contribution to journalArticlepeer-review


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. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating 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 faster 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.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Cloud Computing
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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