The frequent use of location query services in location-based services will come out a large amount of space-time data related to users. Attackers infer information of location or track based on these rich background ...The frequent use of location query services in location-based services will come out a large amount of space-time data related to users. Attackers infer information of location or track based on these rich background knowledge. Therefore, aiming at the problem of trajectory privacy, the context adds instant traffic monitoring based on user behavior patterns, trajectory similarity and other background information. According to the idea of k anonymity, proposed a method combined with traffic condition to protect the trajectory privacy. First, the user randomly selects a time point of the real trajectory to rotate to generate dummy trajectory, and then repeat the above process on the real trajectory and dummy trajectory. Up to the generation of k −1 dummy trajectory, and according to the actual road conditions and trajectory leakage probability, traversing dummy trajectory to adjust. Finally, it is further proved through experiments that the method will be more efficient and protect privacy well.展开更多
k-匿名机制是LBS(location based service)中保证查询隐私性的重要手段.已有文献指出,现有的k-匿名机制不能有效保护连续性查询的隐私性.提出一种连续查询发送模型,该模型融合了查询发送时间的间隔模型和连续性模型,针对此模型下的两种k...k-匿名机制是LBS(location based service)中保证查询隐私性的重要手段.已有文献指出,现有的k-匿名机制不能有效保护连续性查询的隐私性.提出一种连续查询发送模型,该模型融合了查询发送时间的间隔模型和连续性模型,针对此模型下的两种k-匿名算法Clique Cloaking和Non-clique Cloaking,分别提出了一种连续查询攻击算法.在此攻击算法下,匿名集的势不再适合作为查询匿名性的度量,因此提出一种基于熵理论的度量方式AD(anonymityd egree).实验结果表明,对连续性很强的查询,攻击算法重识别用户身份的成功率极高;AD比匿名集的势更能反映查询的匿名性.展开更多
文摘The frequent use of location query services in location-based services will come out a large amount of space-time data related to users. Attackers infer information of location or track based on these rich background knowledge. Therefore, aiming at the problem of trajectory privacy, the context adds instant traffic monitoring based on user behavior patterns, trajectory similarity and other background information. According to the idea of k anonymity, proposed a method combined with traffic condition to protect the trajectory privacy. First, the user randomly selects a time point of the real trajectory to rotate to generate dummy trajectory, and then repeat the above process on the real trajectory and dummy trajectory. Up to the generation of k −1 dummy trajectory, and according to the actual road conditions and trajectory leakage probability, traversing dummy trajectory to adjust. Finally, it is further proved through experiments that the method will be more efficient and protect privacy well.
