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Plume: Differential Privacy at Scale

Plume: Differential Privacy at Scale

27 January 2022
Kareem Amin
Jennifer Gillenwater
Matthew Joseph
Alex Kulesza
Sergei Vassilvitskii
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Papers citing "Plume: Differential Privacy at Scale"

5 / 5 papers shown
Title
Private Count Release: A Simple and Scalable Approach for Private Data
  Analytics
Private Count Release: A Simple and Scalable Approach for Private Data Analytics
Ryan Rogers
37
0
0
08 Mar 2024
Some Constructions of Private, Efficient, and Optimal $K$-Norm and
  Elliptic Gaussian Noise
Some Constructions of Private, Efficient, and Optimal KKK-Norm and Elliptic Gaussian Noise
Matthew Joseph
Alexander Yu
23
2
0
27 Sep 2023
DP-SIPS: A simpler, more scalable mechanism for differentially private
  partition selection
DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection
Marika Swanberg
Damien Desfontaines
Samuel Haney
36
6
0
05 Jan 2023
Hyperparameter Tuning with Renyi Differential Privacy
Hyperparameter Tuning with Renyi Differential Privacy
Nicolas Papernot
Thomas Steinke
135
120
0
07 Oct 2021
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
159
349
0
25 Sep 2021
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