Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/164213
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). https://dx.doi.org/10.29012/JPC.776 | 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. | URI: | https://hdl.handle.net/10356/164213 | ISSN: | 2575-8527 | DOI: | 10.29012/JPC.776 | 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 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. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
lvilhuber,+jpc_manuscript_776-pdfa.pdf | 530.92 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
50
1
Updated on Feb 1, 2023
Page view(s)
13
Updated on Feb 4, 2023
Download(s)
3
Updated on Feb 4, 2023
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.