Privacy‐preserving generative framework for images against membership inference attacks
Privacy‐preserving generative framework for images against membership inference attacks
About this item
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Publisher
Stevenage: John Wiley & Sons, Inc
Journal title
Language
English
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Publisher
Stevenage: John Wiley & Sons, Inc
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Scope and Contents
Contents
Machine learning has become an integral part of modern intelligent systems in all aspects of life. Membership inference attacks (MIAs), as the significant model attacks, also jeopardize the privacy of the intelligent systems. Previous works on defending MIAs concentrate on the model output perturbation or tampering with the training process. Howeve...
Alternative Titles
Full title
Privacy‐preserving generative framework for images against membership inference attacks
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Author / Creator
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TN_cdi_doaj_primary_oai_doaj_org_article_ebbe629198a3474688ce3fe84499e5f4
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ebbe629198a3474688ce3fe84499e5f4
Other Identifiers
ISSN
1751-8628
E-ISSN
1751-8636
DOI
10.1049/cmu2.12507
