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2103.02913
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Quantifying identifiability to choose and audit
ε
ε
ε
in differentially private deep learning
Proceedings of the VLDB Endowment (PVLDB), 2021
4 March 2021
Daniel Bernau
Günther Eibl
Philip-William Grassal
Hannah Keller
Florian Kerschbaum
FedML
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Papers citing
"Quantifying identifiability to choose and audit $ε$ in differentially private deep learning"
2 / 2 papers shown
GCON: Differentially Private Graph Convolutional Network via Objective Perturbation
Jianxin Wei
Yizheng Zhu
Xiaokui Xiao
Ergute Bao
Yin Yang
Kuntai Cai
Beng Chin Ooi
AAML
317
0
0
06 Jul 2024
Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption
Guowen Xu
Guanlin Li
Shangwei Guo
Tianwei Zhang
Hongwei Li
FedML
165
6
0
11 Jul 2022
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