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.

Bibliographic Details
Main Authors: Bao, Ergute, Xiao, Xiaokui, Zhao, Jun, Zhang, Dongping, Ding, Bolin
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/164213
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author Bao, Ergute
Xiao, Xiaokui
Zhao, Jun
Zhang, Dongping
Ding, Bolin
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
author_sort Bao, Ergute
collection NTU
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.
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institution Nanyang Technological University
language English
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spelling ntu-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
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
title 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_short Synthetic data generation with differential privacy via Bayesian networks
title_sort synthetic data generation with differential privacy via bayesian networks
topic Engineering::Computer science and engineering
Differential Privacy
Synthetic Data Generation
url https://hdl.handle.net/10356/164213
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AT zhaojun syntheticdatagenerationwithdifferentialprivacyviabayesiannetworks
AT zhangdongping syntheticdatagenerationwithdifferentialprivacyviabayesiannetworks
AT dingbolin syntheticdatagenerationwithdifferentialprivacyviabayesiannetworks