融合语义信息的时空关联位置隐私保护方法

Method for the protection of spatiotemporal correlation location privacy with semantic information

随着通信网络技术的快速发展、智能电子设备功能的急速提升和定位技术的飞速进步,基于位置的服务使得人们的日常生活更加便捷,然而用户的位置隐私信息却面临着不可忽略的威胁。针对现有面向连续查询的位置隐私保护方法忽略用户移动轨迹中包含的语义信息,导致攻击者可利用这些信息挖掘用户的行为习惯、个人爱好等隐私;同时,传统的假轨迹隐私保护方法往往生成多条假轨迹混淆用户真实轨迹,但是假轨迹中语义位置点的转移不符合用户行为规律,提出了一种融合语义信息的时空关联位置隐私保护方法。该方法将用户历史语义轨迹与位置的语义信息相结合构建了用户行为模型,根据模型中相邻时刻语义位置之间的转移概率和时空关联性构建了符合用户行为规律的假轨迹,实现混淆用户真实轨迹的目的。最后,基于真实数据集将该算法与现有的算法进行比较,表明该算法在攻击者掌握相关背景知识的情况下,可以有效地降低连续查询场景下位置隐私泄露的风险。

With the rapid development of communication network technology,the rapid improvement of intelligent electronic device functions and the rapid advancement of positioning technology,location-based services make people's daily lives more convenient.However,users'privacy information about their locations is facing threats that cannot be ignored.The existing continuous query-oriented location privacy protection methods ignore the semantic information contained in the user's movement trajectories,causing attackers to use that information to mine the user’s behavior habits,personal preferences and other privacies.At the same time,the traditional fake trajectory privacy protection methods generate multiple fake trajectories to confuse the user's real trajectory,but the transfer of semantic location points in the fake trajectory does not conform to the user's behavior rules,so we propose a spatiotemporal correlation location privacy protection method with semantic information.This method combines the historical semantic trajectories of the user with the semantic information on the location to construct a user behavior model,and constructs false trajectories that conform to the rules of user behavior according to the transition probability and temporal-spatial correlation between semantic locations at adjacent moments in the model,thus achieving the purpose of confusing the real trajectory of the user.Finally,based on the real data set,the algorithm is compared with the existing algorithms,which shows that the algorithm can effectively reduce the risk of location privacy leakage in continuous query scenarios when the attacker has relevant background knowledge.