用户协作模式下自适应情境的LBS隐私保护方法

An Adaptive Context-aware LBS Privacy Protection Method Based on Users Collaboration

针对当前LBS(located-based service)应用中位置隐私保护方法难以实现隐私保护度与服务质量平衡的问题,提出了一种基于用户协作的自适应情境的位置隐私保护方法:移动用户消费LBS服务时,需从服务提供商处实时下载"粗糙化"的路网数据,并以此为基础,通过情境感知自主模糊个人真实位置,然后选择具有互助意愿的代理用户进行自适应情境的增量近邻查询。为了提高系统的服务质量,引入通讯成本估算方法确定查询点位置,并采用Voronoi图技术过滤代理用户的查询结果,从而减少系统通讯开销。不仅从理论上分析了该方法的隐私保护度与自适应性,而且对方法性能进行了实验与比较,实验结果表明:该方法较SpaceTwist算法具有更高的性能优势,实现了隐私保护度与服务质量的平衡。

Most current privacy-preserving located-based service（LBS） techniques have proved to be quivering in the balance with the levels of privacy protection and communication overhead.In order to address these challenges,an adaptive collaborative privacy-preserving technique was introduced in which mobile user firstly achieves his location context based on the downloaded rough topology maps from LBS provider’s server while user consume services,then regionalizes his original position and selects a collaborative proxy user for adaptive context-aware incremental nearest-neighbor querying.Meanwhile,in order to improve the QoS（quality-of-service） of system,the query-anchor was determined by communication cost-estimation method and POIs（points of interest） were filtered by on proxy user,which eliminate communication overhead of whole service process.Theoretical analysis and experimental results validated the technique’s effectiveness on LBS accuracy,privacy protection and communication QoS compared with SpaceTwist.