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20172026
most citedLearning Privacy-Preserving Student Networks via Discriminative-Generative Distillation

26 citations · 36 across the 10 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cs.CV2023★ 1 cited

Private Gradient Estimation is Useful for Generative Modeling

Bochao Liu, Pengju Wang, Weijia Guo +4

While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generati…

cs.CR2023

Model Conversion via Differentially Private Data-Free Distillation

Bochao Liu, Pengju Wang, Shikun Li +2

While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment whic…