空间K-匿名技术主要用于隐私保护,防止个人信息泄露。目前的主要方法都基于用户-匿名器-基于位置的服务(location based services,LBS)模型。提出了一种基于位置敏感哈希分割的空间K-匿名共匿算法。这种算法在保距性和共匿性方面都可以...空间K-匿名技术主要用于隐私保护,防止个人信息泄露。目前的主要方法都基于用户-匿名器-基于位置的服务(location based services,LBS)模型。提出了一种基于位置敏感哈希分割的空间K-匿名共匿算法。这种算法在保距性和共匿性方面都可以满足要求,而且算法具有适度的计算复杂度。最后,针对有效性(最小化匿名空间区域)和效率(构建代价)做了实验,证明所提出的算法具有良好的性能。展开更多
Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in or...Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in order to process requests. On the other hand, revealing exact user locations to LBS may pinpoint their identities and breach their privacy. Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. However, existing reciprocal methods rely on a specialized data structure. In contrast, a reciprocal algorithm was proposed using existing spatial index on the user locations. At the same time, an adjusted median splits algorithm was provided. Finally, according to effectiveness (i.e., anonymizing spatial region size) and efficiency (i.e., construction cost), the experimental results verify that the proposed methods have better performance. Moreover, since using employ general-purpose spatial indices, the proposed method supports conventional spatial queries as well.展开更多
文摘空间K-匿名技术主要用于隐私保护,防止个人信息泄露。目前的主要方法都基于用户-匿名器-基于位置的服务(location based services,LBS)模型。提出了一种基于位置敏感哈希分割的空间K-匿名共匿算法。这种算法在保距性和共匿性方面都可以满足要求,而且算法具有适度的计算复杂度。最后,针对有效性(最小化匿名空间区域)和效率(构建代价)做了实验,证明所提出的算法具有良好的性能。
基金National Natural Science Foundation of China(No.61070032)
文摘Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in order to process requests. On the other hand, revealing exact user locations to LBS may pinpoint their identities and breach their privacy. Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. However, existing reciprocal methods rely on a specialized data structure. In contrast, a reciprocal algorithm was proposed using existing spatial index on the user locations. At the same time, an adjusted median splits algorithm was provided. Finally, according to effectiveness (i.e., anonymizing spatial region size) and efficiency (i.e., construction cost), the experimental results verify that the proposed methods have better performance. Moreover, since using employ general-purpose spatial indices, the proposed method supports conventional spatial queries as well.
