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Augment then Smooth: Reconciling Differential Privacy with Certified Robustness

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness

Jiapeng Wu
Atiyeh Ashari Ghomi
David Glukhov
Jesse C. Cresswell
Franziska Boenisch
Nicolas Papernot
    AAML
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Papers citing "Augment then Smooth: Reconciling Differential Privacy with Certified Robustness"

3 / 3 papers shown
Title
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Daniel Kifer
John M. Abowd
Robert Ashmead
Ryan Cumings-Menon
Philip Leclerc
Ashwin Machanavajjhala
William Sexton
Pavel I Zhuravlev
48
26
0
07 Sep 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Zhiqi Bu
Jialin Mao
Shiyun Xu
139
48
0
21 May 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
168
350
0
25 Sep 2021