Personalized trajectory data perturbation algorithm based on quadtree indexing  

作　　者：Liu Kun Jin Junhui Wang Hui Shen Zihao Liu Peiqian 

机构地区：[1]School of Software,Henan Polytechnic University,Jiaozuo 454000,China [2]School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2024年第4期17-27,共11页中国邮电高校学报（英文版）

基　　金：Key Scientific Research Projects of Colleges and Universities in Henan Province(23A520033);Doctoral Scientific Fund of Henan Polytechnic University(B2022-16).

摘　　要：To solve the privacy leakage problem of truck trajectories in intelligent logistics,this paper proposes a quadtreebased personalized joint location perturbation(QPJLP)algorithm using location generalization and local differential privacy(LDP)techniques.Firstly,a flexible position encoding mechanism based on the spatial quadtree indexing is designed,and the length of the encoding can be adjusted freely according to data availability.Secondly,to meet the privacy needs of different locations of users,location categories are introduced to classify locations as sensitive and ordinary locations.Finally,the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category,allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations,thereby improving data availability.Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and top-k classification.

关 键 词：intelligent logistics quadtree indexing local differential privacy(LDP) trajectory privacy protection location categories 

分 类 号：F252[经济管理—国民经济] TP18[自动化与计算机技术—控制理论与控制工程]