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Title: Synthetic data generation with differential privacy via Bayesian networks
Authors: Bao, Ergute
Xiao, Xiaokui
Zhao, Jun
Zhang, Dongping
Ding, Bolin
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: 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).
Project: MOE2018-T2-2-091
Journal: Journal of Privacy and Confidentiality
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.
ISSN: 2575-8527
DOI: 10.29012/JPC.776
Schools: School of Computer Science and Engineering 
Rights: © 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 or send a letter to Creative Commons, 171 Second St, Suite300, San Francisco, CA 94105, USA, or Eisenacher Strasse 2, 10777 Berlin, Germany.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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