[1]ZHANG Xing,CHEN Hao.A research review of high-dimensional data publishing based on a differential privacy model[J].CAAI Transactions on Intelligent Systems,2021,16(6):989-998.[doi:10.11992/tis.202104023]
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A research review of high-dimensional data publishing based on a differential privacy model
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 6
Page number:
989-998
Column:
综述
Public date:
2021-11-05
- Title:
- A research review of high-dimensional data publishing based on a differential privacy model
- Keywords:
- big data publishing; privacy protection; data mining; feature dimension reduction; bayesian network; rough set; random projection; high dimensional data; differential privacy
- CLC:
- TP309.2
- DOI:
- 10.11992/tis.202104023
- Abstract:
- With the advent of the era of big data, the amount of digitally-generated information has increased dramatically, and the data dimension has also shown geometric growth. How to fully mine high-dimensional data while maintaining the user’s privacy has become a focus and a difficult research topic in the field of big data publishing. As a powerful privacy protection model, differential privacy is increasingly in use in high-dimensional data publishing. This paper summarizes the application of differential privacy and its related methods in high-dimensional data publishing, focusing on an analysis of the advantages and disadvantages of differential privacy and feature dimension reduction, feature extraction, the Bayesian network, tree model, and the latest rough set and random projection methods in high-dimensional data publishing. Moreover, we survey the application and comparison of each method in high-dimensional data and finally discuss the future application of differential privacy in high-dimensional data publishing.
- References:
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2021-12-25