19 citations · 29 across the 5 of their papers we have counts for
5 papers
Enabling Privacy-Preserving and Publicly Auditable Federated Learning
Huang Zeng, Anjia Yang, Jian Weng +4
Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there…
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…
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…
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…
DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks
Hongwei Huang, Weiqi Luo, Guoqiang Zeng +3
Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while…