Abstract
Although mobile crowdsensing (MCS) has become a new paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, sharing the sensory data with users’ sensitive location data may expose them to potential privacy. Differential privacy is a popular privacy preservation approach, which could realize strong privacy protection in various scenarios, ranging from data collection, data releasing, to data analysis. In this paper, we focus on the noise adding mechanism in constructing differentially private spatial decomposition. The noise adding mechanism, as the standard approach to preserving differential privacy, directly affects the utility of differentially private data. To improve the accuracy of counting query on the private two-dimensional spatial datasets, we propose a Staircase mechanism based differentially private Uniform grid partition method, namely Staircase_Ugrid. We first investigate the relationship between non-uniform error and query intersection area, and utilize the linear least square to fit the linear relation between them. Then we deduce the optimal partition granularity by minimizing non-uniform error and noise error. In the experiments, we use two real world datasets to evaluate the performance of the proposed method. Experiments show that the proposed two-dimensional spatial publishing method makes a good trade-off between data privacy and utility.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant 61702148 and Grant 61672648.
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Liu, Q., Han, J., Yao, X., Yu, J., Lu, J., Peng, H. (2020). Differential Private Spatial Decomposition for Mobile Crowdsensing Using Staircase Mechanism. In: Wang, J., Chen, L., Tang, L., Liang, Y. (eds) Green, Pervasive, and Cloud Computing – GPC 2020 Workshops. GPC 2020. Communications in Computer and Information Science, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4532-4_1
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