14 citations · 18 across the 2 of their papers we have counts for
4 papers · 1 filter
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu +6
Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the tw…
Large Scale Private Learning via Low-rank Reparametrization
Da Yu, Huishuai Zhang, Wei Chen +2
We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing indivi…
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu, Huishuai Zhang, Wei Chen +1
The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private…
How Does Data Augmentation Affect Privacy in Machine Learning?
Da Yu, Huishuai Zhang, Wei Chen +2
It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposi…