Fusion: Efficient and Secure Inference Resilient to Malicious Servers
Fusion: Efficient and Secure Inference Resilient to Malicious Servers
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Author / Creator
Dong, Caiqin , Weng, Jian , Jia-Nan, Liu , Zhang, Yue , Yao, Tong , Yang, Anjia , Cheng, Yudan and Hu, Shun
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
In secure machine learning inference, most of the schemes assume that the server is semi-honest (honestly following the protocol but attempting to infer additional information). However, the server may be malicious (e.g., using a low-quality model or deviating from the protocol) in the real world. Although a few studies have considered a malicious...
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Full title
Fusion: Efficient and Secure Inference Resilient to Malicious Servers
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Record Identifier
TN_cdi_proquest_journals_2661271846
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2661271846
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2205.03040
