Serious location privacy problems arise with extensive application of location-based services. Nowadays, location k-anonymity is the one of the most popular location privacy-preserving methods, it requires a trusted third party as an anonymity server which is proved to be the performance bottleneck and aim point of attacks. In addition, it complicates the query processing by requiring an anonymity region instead of a point. This paper proposes a collaborative location privacy-preserving method without anonymity server and cloaking region. Anonymity groups are formed through user's collaboration, members in the group regard density center as their locations when requiring for LBS, and acquire kNN results incrementally from the service provider. At last, group members get the precise results through computing distances between their locations and the ￡NN results. The proposed method achieves k-anonymity without cloaking region, the efficiency of anonymity is improved, and query processing is simplified. Extensive experimental results show advantages of the proposed method.%基于位置的服务的广泛应用给人们的生活带来了极大的便利.但是用户在享受这些便利服务的同时,个人的位置隐私也面临着严重的威胁.目前,典型的位置隐私保护技术是基于中心服务器的位置k-匿名方法.该方法容易使中心服务器成为性能瓶颈和集中攻击点,也容易造成查询处理过程的复杂化,且牺牲了用户的服务质量.文中提出了一种用户协作无匿名区域的隐私保护方法CoPrivacy,该方法通过用户之间协作形成匿名组,匿名组内的用户用该组的密度中心代替真实位置发出查询,并增量地从服务器获得近邻查询结果.组内成员通过近邻查询结果与自身位置之间的距离计算得出精确的查询结果.CoPrivacy在不使用匿名区域的情况下达到了k-匿名的效果,不牺牲用户的服务质量,并且提高了匿名系统的整体性能,简化了服务提供商的查询处理过程.文中在真实数据和模拟数据集上进行了充分的实验,验证了该方法的优越性.
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