In recent years, along with the rapid development of mobile computing technology and wireless devices, LBS privacy protection technology has been given a lot of attention. A lot of anonymity algorithms have been designed to protect the privacy of the mobile users. However, k-anonymity for location privacy and the 1-diversity query mechanisms for privacy mechanisms are applicable only to a snapshot query (snapshot query), not suitable for continuous queries. If the existing static anonymous continuous query algorithm is directly applied, it will generate a lot of problems such as having a privacy disclosure, or an overwhelming work of anonymous servers. It presents a query model based on the query identifier. Define a continuous query identifier for each task,LBS returns the result of query to the anonymous server according to the query identifier. The attackers collect the same query identifier anonymity set. The attackers can infer the private information with comparison, which leads to user privacy disclosure. To address this sort of problems,set up many pairs of k-table in the anonymous server, whenever a user sends a query, the anonymous server query the table, randomly selecting a number as the query identifier from this table. Attackers can not collect every element of the anonymous set of a continuous query task with this way, so the user privacy can be well protected.%近年来,伴随着移动计算技术和无限设备的蓬勃发展,LBS中的隐私保护技术受到了学术界的广泛关注,提出了很多匿名算法以保护移动用户的隐私信息.但是针对位置隐私的k匿名机制和查询隐私的1-diversity机制都只是通用于快照查询( snapshot query),不能适用于连续查询.如果将现有的静态匿名算法直接应用于连续查询,将会产生隐私泄露、匿名服务器工作代价大等问题.文中提出了一种基于查询标识符的查询模型,对于每一个连续查询任务都定义一个标识符,LBS通过这个标识符返回给匿名服务器查询内容,攻击者收集标识符相同的查询任务匿名集,对其进行比较和推断,导致用户隐私泄露.针对这个问题,在匿名服务器里设置一张一对k的表,每当用户发送一个查询时,匿名服务器查询这个表,从这个表中随机选取一个数作为这次查询的标识符.这样攻击者收集到匿名集就不会是一个连续查询任务的全部匿名集,在一定程度和时间上保护了用户的隐私.
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