z-logo
open-access-imgOpen Access
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data
Author(s) -
March Boedihardjo,
Thomas Strohmer,
Roman Vershynin
Publication year - 2022
Publication title -
siam journal on mathematics of data science
Language(s) - English
Resource type - Journals
ISSN - 2577-0187
DOI - 10.1137/21m1449944
Subject(s) - differential privacy , computer science , synthetic data , data mining , data sharing , construct (python library) , information privacy , sampling (signal processing) , theoretical computer science , algorithm , computer security , medicine , alternative medicine , filter (signal processing) , pathology , computer vision , programming language

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here