Method of Local Differential Privacy Method for High-Dimensional Data Based on Improved Bayesian Network

doi:10.3969/j.issn.1671-1122.2023.02.003

Abstract

Abstract:

In this paper, a local differential privacy method for high-dimensional data based on improved Bayesian network was proposed. By using the differential privacy protection algorithm of data source, the client data set was disturbed to generate the disturbed data set, so that the privacy of the local original data set was protected, and the privacy security of users was fundamentally protected. Then the high-dimensional data set was reduced to several low-dimensional attribute sets by the improved Bayesian network, and the new data set was finally synthesized，and the artificial bee colony algorithm was used to further improve the construction of Bayesian network structure. Finally, the experimental results show that the research method in this paper has advantages in data practicability, and the Bayesian network structure achieved better convergence.

Key words: local differential privacy, Bayesian network, artificial bee colony algorithm, high-dimensional data

CLC Number:

TP309

ZHAO Jia, GAO Ta, ZHANG Jiancheng. Method of Local Differential Privacy Method for High-Dimensional Data Based on Improved Bayesian Network[J]. Netinfo Security, 2023, 23(2): 19-25.

Figures/Tables 2

References 23

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