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Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data
13 July 2021
M. Boedihardjo
Thomas Strohmer
Roman Vershynin
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Papers citing
"Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data"
3 / 3 papers shown
Title
Synthetic Data -- what, why and how?
James Jordon
Lukasz Szpruch
F. Houssiau
M. Bottarelli
Giovanni Cherubin
Carsten Maple
Samuel N. Cohen
Adrian Weller
43
109
0
06 May 2022
Private sampling: a noiseless approach for generating differentially private synthetic data
M. Boedihardjo
Thomas Strohmer
Roman Vershynin
SyDa
29
14
0
30 Sep 2021
Leveraging Public Data for Practical Private Query Release
Terrance Liu
G. Vietri
Thomas Steinke
Jonathan R. Ullman
Zhiwei Steven Wu
158
58
0
17 Feb 2021
1