基于时空关联和位置语义的个性化假位置生成方法

基于假位置的一类隐私保护方案在保护用户位置隐私的同时能够使用户获得准确查询信息，并无需依赖第三方和共享密钥.然而，当攻击者掌握一定的背景知识，例如道路时空可达信息、位置特征和用户的历史请求统计特性等，会导致假位置被识别的概率升高，降低隐私保护程度.针对上述问题，提出了基于时空关联和位置语义的个性化假位置生成算法.首先根据与前一次请求位置连续可达的条件产生假位置，然后通过建立语义树筛选出与真实位置语义相近的假位置，最后进一步筛选出与用户历史请求统计特性最接近的假位置.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，可以有效地降低位置隐私泄露的风险.

Personalized Dummy Generation Method Based on Spatiotemporal Correlations and Location Semantics

Without the need for the third party and key sharing, the dummy-based privacy protection scheme enables the users to obtain precise query results while protecting their location privacy. However, when the adversary has certain background knowledge, e.g., the spatiotemporal reachability information, the location semantics, the users'' historic query statistics, the probability of dummies being inferred will rise and the degree of privacy protection will be reduced. To solve this problem, a personalized dummy generation method based on spatiotemporal correlations and location semantics is proposed. Dummies are first generated based on the continuous reachability with previous request locations, and then filtered through the check of location semantic similarity and finally filtered by accessibility to user''s historic query statistics. Experiments based on real datasets show that the proposed dummy generation method can effectively reduce the risk of privacy disclosure compared with current two dummy generation methods, especially when the adversary has related background knowledge.