Preliminary study about advantageous trajectory anonymization methods based on population
Toshiro Hikita, Rie Shigetomi Yamaguchi · 2018
Growing mobile networks and widely spread of Global Positing System (GPS) devices enables to collect large scale location and trajectory data. Using trajectory data to succeed, privacy and data characteristics are essential. Most anonymization methods are losing characteristic for service providers, like adding noise to trajectories. In this paper, firstly we present adaptive quadtree grid population calculation to determine grid size of trajectories. Our anonymization method dynamically adjust cluster size to maintain trajectory data characteristics, small mesh to the dense populated area, large mesh to sparse populated area, base on quadtree geospatial data structure. Proposed method is satisfied that adaptive size scaling and more efficient to maintain characteristic. Our experiment suggest that proposed method correctly anonymize Tokyo Urban flow data of 1.3M Trajectories.