面向连续位置服务的位置感知隐私保护方案

为了增强在位置服务（LBS）中对用户个人隐私的保护，提出基于本地缓存的位置感知匿名选择算法（LaSA）. 利用历史轨迹信息和缓存信息，以不依赖可信第三方（TTP）服务器的方式构建匿名区域. 在连续位置服务查询中，利用马尔可夫预测模型对未来可能查询的位置进行预判. 根据预测位置、缓存贡献度和数据新鲜度构建匿名区域，以覆盖用户所查真实区域. 结果表明，与已有方案相比，所提出的LaSA隐私保护方案能提供更高的缓存命中率，减少用户服务请求次数，保证用户位置数据的安全.

Location-aware privacy protection scheme in continuous location-based service

A location-aware anonymous selection algorithm (LaSA) based on local cache was proposed in order to enhance the protection of users′ privacy in location-based service (LBS). Anonymous zones were constructed without relying on the trusted third-party (TTP) server, using historical track information and cache information. The Markov prediction model was used to select the possible query location in the future continuous location service query. The anonymous region was constructed to cover the region inspected by user based on predicted location, cache contribution, and data freshness. Results show that the proposed LaSA solution can provide a higher cache hit rate reduce the number of user service requests, and ensure the security of user′s location data with comparison with the existing schemes.