Résumé: Statistical partitioning and publishing is a common strategy employed in location-based big data services. This paper proposes a deep learning-based statistical partitioning structure prediction method and a differential publishing method to address the problems of unreasonable structures and low efficiency in traditional statistical partitioning and publishing of location-based big data. The 2D space has been finely partitioned and merged bottom-up to construct a reasonable partition structure. The partition structure matrices have been organized into a 3D spatio-temporal sequence and the spatio-temporal characteristics are extracted via a deep learning model to realize the prediction of the partition structure. Differential privacy budget allocation and Laplace noise incorporation have been implemented on the predicted partition structure to realize the privacy protection of the statistical partitioning and publishing of location-based big data. Experimental comparison of the real-world datasets suggests the advantages of the envisaged method in improving the querying accuracy of the published data and the execution efficiency of the publishing algorithm.

Langue: Anglais