Abstract
As an important means of data analysis, frequent itemset mining is widely used in the field of big data. In recent years, local differential privacy has become a representative privacy protection technology in the field of frequent itemset mining due to its good mathematical theory, which has attracted the continuous attention of researchers. The existing frequent itemset mining methods based on local differential privacy have problems with insufficient data availability. Aiming at the existing binary coding-based perturbation method that causes large matching errors, an improved data perturbation method is proposed to enhance the availability of mining results while protecting data privacy. To solve the large privacy budget of existing methods, the hidden Markov model is introduced to avoid accessing a huge quantity of itemset. Thus, the candidate set can be quickly generated, which improves the efficiency of the algorithm. Experimental results show that the proposed method has a lower privacy budget and higher data accuracy.
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Acknowledgement
This work is supported by the state Grid Jiangsu Electric Power Corporation Project (J2020113).
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Wu, N., Zou, Y., Shan, C. (2021). A Frequent Itemset Mining Method Based on Local Differential Privacy. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_20
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DOI: https://doi.org/10.1007/978-3-030-87571-8_20
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