A Differential Privacy K-Means Algorithm for Improving Privacy Budget Allocation
Authors: 
Sen Liu, Jianhua LiuAuthors Info & Claims
Sen Liu, Jianhua LiuAuthors Info & ClaimsPages 96 - 101
Abstract
As a privacy protection method with strict mathematical definition, differential privacy has been widely used in various fields of data mining including clustering algorithm. However, the traditional differential privacy k-means algorithm is sensitive to the selection of initial value, and the allocation of privacy budget is relatively single, which reduces the availability of the algorithm. In order to further improve the availability of the differential privacy K-means algorithm, this paper proposes a privacy budget allocation method combining error analysis to optimize algorithm iteration times and merge clustering, and carries out theoretical analysis and experimental verification at the same time. The results show that the algorithm not only satisfies the definition of differential privacy, but also improves the availability of clustering effectively.
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- A Differential Privacy K-Means Algorithm for Improving Privacy Budget Allocation
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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