School of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China.
Sci Rep. 2022 Dec 8;12(1):21254. doi: 10.1038/s41598-022-25175-5.
The mobility data of citizens provide important information on the epidemic spread including Covid-19. However, the privacy versus security dilemma hinders the utilization of such data. This paper proposed a method to generate pseudo mobility data on a per-agent basis, utilizing the actual geographical environment data provided by LBS to generate the agent-specific mobility trajectories and export them as GPS-like data. Demographic characteristics such as behavior patterns, gender, age, vaccination, and mask-wearing status are also assigned to the agents. A web-based data generator was implemented, enabling users to make detailed settings to meet different research needs. The simulated data indicated the usability of the proposed methods.
公民的移动数据为包括新冠疫情在内的传染病传播提供了重要信息。然而,隐私与安全之间的权衡难题阻碍了此类数据的利用。本文提出了一种方法,能够在每个代理的基础上生成虚假的移动数据,利用 LBS 提供的实际地理环境数据来生成特定代理的移动轨迹,并将其作为类似于 GPS 的数据导出。代理还被分配了行为模式、性别、年龄、疫苗接种和戴口罩状态等人口统计学特征。我们实现了一个基于网络的数据集生成器,使用户能够进行详细设置以满足不同的研究需求。模拟数据表明了所提出方法的可用性。