VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any obstruction by the malicious model holder during the verification process. We rely on secure two-party computation (2PC) technology to implement VerifyML, and carefull...
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VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
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TN_cdi_proquest_journals_2725737168
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2725737168
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2331-8422
