Differential Privacy High-dimensional Data Publishing Method Based on Bayesian Network-Reference-Cited by-同舟云学术

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Differential Privacy High-dimensional Data Publishing Method Based on Bayesian Network

Published:2022-07 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)
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Author:

Lu Xiaotian¹,Piao Chunhui¹,Han Jianghe²

Affiliation:

1. School of Information Science and Technology, Shijiazhuang Tiedao University,Hebei key Laboratory of Electromagnetic Environmental Effects and Information Processing,Shijiazhuang,Hebei,China

2. School of Information Science and Technology, Shijiazhuang Tiedao University,Shijiazhuang,Hebei,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9853204/9853314/09853392.pdf?arnumber=9853392

Reference21 articles.

1. PrivBayes[J];zhang;ACM Transactions on Database Systems (TODS),2017

2. Differentially private data cubes

3. Survey on feature dimension reduction for high-dimensional data[J];hu;Application Research of Computers,2008

4. LSI_LDA: mixture method for feature dimensionality reduction[J];shi;Application Research of Computers,2017

5. Privacy data publishing method based on Bayesian network and semantic tree[J];hao;Computer Engineering,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. How Resilient is Privacy-preserving Machine Learning Towards Data-Driven Policy? Jakarta COVID-19 Patient Study Case;Proceedings of the 2023 Workshop on Recent Advances in Resilient and Trustworthy ML Systems in Autonomous Networks;2023-11-26

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