pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
About this item
Full title
Author / Creator
Weng, Jiasi , Weng, Jian , Tang, Gui , Yang, Anjia , Li, Ming and Jia-Nan, Liu
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN performance over non-public data from multiple testers, while respecting model privacy. To balance the security and efficiency issues, three new efforts are done b...
Alternative Titles
Full title
pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2622680070
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2622680070
Other Identifiers
E-ISSN
2331-8422
