基于情景感知的位置隐私保护方法

A context-aware location privacy-preserving technique

在不改变现有LBS系统架构下,提出了一种自适应情景的位置隐私保护方法:首先对服务器端存储的大规模路网进行粗糙化处理以适应终端的实时下载,然后终端以此数据为基础,通过上下文感知获取用户位置情景信息,并自适应用户个性化设置,在保证位置隐私保护强度的前提下,实现最小通信开销代价的增量近邻查询.论文不仅从理论上分析了该方法的安全性,而且通过实验与SpaceTwist方法进行性能比较,验证了该算法具有较高的性能.

Most current privacy-preserving located-based service(LBS)techniques either require a trusted third-party anonymizer(TTP)or have proved to be quivering in the balance with the levels of privacy protection and communication overhead.In order to address these challenges,a context-aware privacy-preserving technique without TTP was presented:mobile user firstly achieved his location context based on the downloaded rough topology maps from LBS provider′s server while user consumes services.Meanwhile,the usage of context-aware technique ensured adaptability to the user′s personalized privacy settings,and enabled the LBS provider′s server to retrieve the minimal incremental nearest-neighbor query results.Theoretical analysis and experimental results validate the technique′s effectiveness on LBS accuracy,privacy protection and communication QoS(quality-of-service) compared with SpaceTwist.