Location Big Data Partition and Publishing Method based on Sampling and Adjustment

摘要

In order to maintain a trade-off between preservedprivacy and enhanced utility of data publishing, a partitionand publishing method for location big data is proposedbased on sampling and adjustment. Firstly, the sampling withfixed time interval is used to simulate the publishing processof location big data, and differential processing method isdesigned to reduce the temporal and spatial redundancy ofadjacent snapshots. Then, data update status at the currenttime is determined by the result of differential processing.Corresponding adjustment methods are designed for the grid-based and tree-based partition structure, and Laplace noise isadded to the adjusted structure in order to realize differentialprivacy protection for the published data. Experiments showthat the proposed partition and publishing method has largeradvantages in improving regional query accuracy and theefficiency of algorithm.