Trajectory privacy data publishing scheme based on local optimisation and R-tree

The proliferation of location-based service applications has led to a substantial surge in the amount life trajectory data produced by mobile devices. And these frequently contain confidential personal details. Simultaneously, corresponding relatively lagging privacy protection technology and improper handling method will make tremendous problems with breach. Therefore, this paper presents publishing scheme, denoted as LORDP, which is based on local optimisation R-tree. proposed scheme aims handle sensitive while improves effectiveness. Firstly, combines LKC-privacy model requirement filter out minimum violating sequences set, reduce sensitivity injected noise. Secondly, R-tree constructed similarity. Finally, Laplacian noise added R-tree’s leaf nodes constrained differential privacy. experiments show that LORDP algorithm significantly enhances utility compared other algorithms, reduces loss rate about approximately 2% for per data, shows present extremely effective.