一种基于假数据的新型轨迹隐私保护模型

Novel Trajectory Privacy Preserving Mechanism Based on Dummies

基于位置服务的普及给人们的生活带来了极大的便利,但同时也带来了严重的隐私泄露问题。基于假轨迹的隐私保护技术是目前比较流行的一种方法,但是现有的大多数假轨迹方法没有考虑到用户的个性化需求。基于此问题,提出了一种改进的隐私保护模型,并按照这个模型设计了一个假轨迹生成算法。该模型包含5个参数,分别命名为短期位置暴露概率、长期轨迹暴露概率、轨迹偏移距离、轨迹局部相似度和服务请求概率,用户可以通过自身需求自定义这些度量,并通过假轨迹生成算法来生成假轨迹,从而避免隐私的泄露。实验结果表明,该算法可以在满足相同隐私的条件下生成较少的假轨迹,尤其是考虑了服务请求概率这一背景信息,该模型在保护移动对象轨迹隐私方面比之前的方案更有效。

The popularity of location-based services（LBS）has brought great convenience to people＇s life,but it also brings serious privacy leakage at the same time.Dummy is a popular technology at present,but most existing methods do not take the individual needs of users into account.To address this problem,a improved privacy model was proposed,guided by which we designed a dummy trajectories generation algorithm.The model includes five reasonable parameters,namely short-term disclosure,long-term disclosure,trajectories distance deviation,trajectories local similarity and services request probability.People can customize these metrics through their own needs and generate dummies by dummy trajectories generation algorithm to avoid leakage of privacy.The experiment results show that the algorithm can generate fewer dummy trajectories to satisfy the same privacy-preserving requirement.So it＇s more effective than existing works in preserving movement trajectories,especially the probability of services request is discussed.