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Synthetic Data Generation with Differential Privacy via Bayesian Networks

Authors :
Ergute Bao
Xiaokui Xiao
Jun Zhao
Dongping Zhang
Bolin Ding
Source :
The Journal of Privacy and Confidentiality, Vol 11, Iss 3 (2021)
Publication Year :
2021
Publisher :
Labor Dynamics Institute, 2021.

Abstract

This paper describes PrivBayes, a differentially private method for generating synthetic datasets that was used in the 2018 Differential Privacy Synthetic Data Challenge organized by NIST.

Details

Language :
English
ISSN :
25758527
Volume :
11
Issue :
3
Database :
Directory of Open Access Journals
Journal :
The Journal of Privacy and Confidentiality
Publication Type :
Academic Journal
Accession number :
edsdoj.77b6c76a597f433795ac5a2eb6140be8
Document Type :
article
Full Text :
https://doi.org/10.29012/jpc.776