8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CR2024
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…
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.CR2020★ 8 cited
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…