Towards realizing dynamic statistical publishing and privacy protection of location-based data: An adaptive sampling and grid clustering approach

To realize dynamic statistical publishing and protection of location-based data privacy, this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment. The PID control strategy is combined with the difference in data variation to realize the dynamic adjustment of the data publishing intervals. The spatial-temporal correlations of the adjacent snapshots are utilized to design the grid clustering and adjustment algorithm, which facilitates saving the execution time of the publishing process. The budget distribution and budget absorption strategies are improved to form the sliding window-based differential privacy statistical publishing algorithm, which realizes continuous statistical publishing and privacy protection and improves the accuracy of published data. Experiments and analysis on large datasets of actual locations show that the privacy protection algorithm proposed in this paper is superior to other existing algorithms in terms of the accuracy of adaptive sampling time, the availability of published data, and the execution efficiency of data publishing methods.