16 citations · 35 across the 7 of their papers we have counts for
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cs.LG2020
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Xiaoxiao Li, Yangsibo Huang, Binghui Peng +2
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data…
cs.LG2020★ 8 cited
Privacy-preserving Learning via Deep Net Pruning
Yangsibo Huang, Yushan Su, Sachin Ravi +3
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step to…