k-prototype聚类算法和相对熵下敏感数据重发布隐私安全保护

doi:10.13229/j.cnki.jdxbgxb.20240077

Abstract

Abstract:

In order to effectively ensure the security and privacy of sensitive attribute data republication， a privacy protection method for sensitive attribute data republication based on k-prototype clustering algorithm and relative entropy is proposed. Based on the type attributes of sensitive attribute data， fast clustering is used to calculate the distance between each data type attribute， and k-prototype clustering algorithm is used to cluster sensitive attribute data. Using relative entropy to calculate the sensitivity of sensitive attribute data after clustering， a weighted multi-dimensional bucket group is constructed based on the sensitivity to unfold sensitive attribute data and republish it anonymously， ultimately achieving the goal of privacy protection. The experimental results show that the proposed method can effectively reduce the concealment rate， information loss， and information disclosure of private data， improve the degree of privacy protection for sensitive attribute data republishing， and ensure the security of data.

Key words: k-prototype clustering algorithm, relative entropy, sensitive attribute data, republish, privacy protection

CLC Number:

TP393

Qi-liang ZHU,Xue-ting YU. k⁃prototype clustering algorithm and privacy protection for sensitive data republishing under relative entropy[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 1009-1014.

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References 10

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k⁃prototype clustering algorithm and privacy protection for sensitive data republishing under relative entropy

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