19 citations · 29 across the 5 of their papers we have counts for
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cs.CR2022★ 1 cited
VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder
Guowen Xu, Xingshuo Han, Gelei Deng +5
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 a…
cs.CR2022★ 19 cited
Fusion: Efficient and Secure Inference Resilient to Malicious Servers
Caiqin Dong, Jian Weng, Jia-Nan Liu +5
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). Ho…
cs.CR2022★ 1 cited
pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
Jiasi Weng, Jian Weng, Gui Tang +3
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 truthf…