12 citations · 15 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Jie Fu, Qingqing Ye, Haibo Hu +4
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and member…
cs.LG2023★ 1 cited
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Huifa Li, Jie Fu, Zhili Chen +3
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-c…
cs.CR2023
Truncated Laplace and Gaussian mechanisms of RDP
Jie Fu, Zhiyu Sun, Haitao Liu +1
The Laplace mechanism and the Gaussian mechanism are primary mechanisms in differential privacy, widely applicable to many scenarios involving numerical data. However, due to the i…