Abstract
The problem of information leakage is becoming more and more serious, and how to effectively protect the security of personal privacy information has become an urgent problem at present. To this end, this paper proposes an interactive query differential privacy protection model based on big data analysis. The model is based on the irrelevance processing of differential privacy association rules to find out the hidden association information among trajectory data sets. The model uses the shared prefix method to de-compress the trajectory dataset deeply. The model constructs prefix trees to clarify useful information and remove redundant trajectory data. The model constructs a parallel gradient descent matrix decomposition algorithm based on the low-rank mechanism and alternating direction multiplier method to remove the load matrix built from the initial results and decompose the corresponding irrelevant load matrix. The model then takes reasonable values according to the user authority level to limit the privacy budget maximum. The model uses Laplace’s principle to add the determined reasonable noise to the differential privacy trajectory data, reduce and delete the irrelevant attribute trajectory data, and finally feed the query results to the user. Through simulation and comparison experiments, we conclude that the proposed model can make the privacy information available to the highest degree and minimize the success probability of stealers, and thus the differential privacy track data can be protected to the best effect.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China (2020YFB1005804), and in part by the National Natural Science Foundation of China under Grants 61632009, 61872097, and 61802076; and in part by the Natural Science Foundation of Guangdong Province (2022A1515011386). The Basic and Applied Basic Research Fund of Guangdong Province No. 2019A1515111080.
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Feng, G., Yang, W., Peng, T., Xing, X., Chen, S., Li, Y. (2023). An Interactive Query Differential Privacy Protection Model Based on Big Data Analysis. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_23
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