摘要
现有的轨迹隐私保护技术大多是对移动对象的静态轨迹数据进行保护,却忽略了移动对象动态轨迹依然存在隐私泄露的风险。针对此问题,提出基于遗传算法的动态轨迹匿名算法。利用遗传算法搜索全局最优解的特性,在移动对象当前时间段内的历史轨迹中建立轨迹行为模式,通过轨迹行为模式预测移动对象的轨迹,根据移动对象新增的预测轨迹不断更新轨迹行为模式,使得轨迹预测的准确性更高。对于新增的预测轨迹采用轨迹K-匿名技术进行匿名轨迹生成,以达到保护移动对象个体隐私信息的目的。实验表明,与现有的轨迹匿名算法相比,所提算法在保护轨迹隐私的同时进一步提高了轨迹数据质量。
Most of the existing trajectory privacy protection technologies protect the static trajectory data of mobile objects,but ignore the risk of privacy disclosure of the dynamic trajectory of mobile objects.In order to solve this problem,this paper studies the dynamic trajectory anonymity based on genetic algorithm.The proposed algorithm uses the characteristics of genetic algorithm to search the global optimal solution,establishes the track behavior mode in the current historical track of the moving object,forecasts the track of the moving object through the track behavior mode,and constantly updates the track behavior mode according to the new predicted track of the moving object,so as to achieve higher accuracy of track prediction.In order to protect the privacy information of the mobile object,K-anonymity technology is used to generate the false trajectory for the new prediction trajectory.Expe-riments show that,compared with the existing track anonymity algorithm,the proposed algorithm can protect the privacy of the track and further improve the quality of the track data.
作者
贾俊杰
秦海涛
JIA Jun-jie;QIN Hai-tao(School of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第1期142-150,共9页
Computer Engineering & Science
基金
国家自然科学基金(61967013)
甘肃省高等学校创新能力提升项目(2019A-006)。
关键词
轨迹隐私
动态轨迹
遗传算法
轨迹行为模式
预测轨迹
trajectory privacy
dynamic trajectory
genetic algorithm
trajectory behavior mode
predicted trajectory
