Synthetic data generation with differential privacy via Bayesian networks

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.

Saved in:

Bibliographic Details
Main Authors:	Bao, Ergute, Xiao, Xiaokui, Zhao, Jun, Zhang, Dongping, Ding, Bolin
Other Authors:	School of Computer Science and Engineering
Format:	Article
Language:	English
Published:	2023
Subjects:	Engineering::Computer science and engineering Differential Privacy Synthetic Data Generation
Online Access:	https://hdl.handle.net/10356/164213
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-164213
record_format	dspace
spelling	sg-ntu-dr.10356-1642132023-01-10T00:59:47Z Synthetic data generation with differential privacy via Bayesian networks Bao, Ergute Xiao, Xiaokui Zhao, Jun Zhang, Dongping Ding, Bolin School of Computer Science and Engineering Engineering::Computer science and engineering Differential Privacy Synthetic Data Generation 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was supported by the Ministry of Education Singapore (Number MOE2018-T2-2-091), and by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the funding agencies. 2023-01-10T00:59:47Z 2023-01-10T00:59:47Z 2021 Journal Article Bao, E., Xiao, X., Zhao, J., Zhang, D. & Ding, B. (2021). Synthetic data generation with differential privacy via Bayesian networks. Journal of Privacy and Confidentiality, 11(3). https://dx.doi.org/10.29012/JPC.776 2575-8527 https://hdl.handle.net/10356/164213 10.29012/JPC.776 2-s2.0-85123691224 3 11 en MOE2018-T2-2-091 Journal of Privacy and Confidentiality © E. Bao, X. Xiao, J. Zhao, D. Zhang, and B. Ding. This work is licensed under the Creative Commons License Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/ or send a letter to Creative Commons, 171 Second St, Suite300, San Francisco, CA 94105, USA, or Eisenacher Strasse 2, 10777 Berlin, Germany. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering Differential Privacy Synthetic Data Generation
spellingShingle	Engineering::Computer science and engineering Differential Privacy Synthetic Data Generation Bao, Ergute Xiao, Xiaokui Zhao, Jun Zhang, Dongping Ding, Bolin Synthetic data generation with differential privacy via Bayesian networks
description	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.
author2	School of Computer Science and Engineering
author_facet	School of Computer Science and Engineering Bao, Ergute Xiao, Xiaokui Zhao, Jun Zhang, Dongping Ding, Bolin
format	Article
author	Bao, Ergute Xiao, Xiaokui Zhao, Jun Zhang, Dongping Ding, Bolin
author_sort	Bao, Ergute
title	Synthetic data generation with differential privacy via Bayesian networks
title_short	Synthetic data generation with differential privacy via Bayesian networks
title_full	Synthetic data generation with differential privacy via Bayesian networks
title_fullStr	Synthetic data generation with differential privacy via Bayesian networks
title_full_unstemmed	Synthetic data generation with differential privacy via Bayesian networks
title_sort	synthetic data generation with differential privacy via bayesian networks
publishDate	2023
url	https://hdl.handle.net/10356/164213
_version_	1756370556387590144

Synthetic data generation with differential privacy via Bayesian networks

Similar Items