中文摘要：

针对k-匿名机制无法保证位置信息服务（LBS）中连续查询隐私性的问题，提出一种连续查询发送模型，该模型融合了查询发送时间的间隔模型和连续性模型．在该模型的基础上针对k-匿名算法，提出一种连续查询攻击算法，该算法将和连续查询相关的一系列快照互相关联，计算出快照的匿名集内每个用户发送查询的概率，从而估计出查询真正的发送者．仿真实验模拟在不同的连续性参数、匿名集的势的情况下，使用连续查询攻击算法重识别受k-匿名保护的查询．通过对被恶意攻击者重识别的查询数量统计，结果表明，对连续性很强的查询，攻击算法重识别用户身份的成功率极高（85％），比不使用攻击算法所获得的重识别率提高了1．5倍以上，严重破坏了查询的匿名性．

英文摘要：

K-anonymization cannot effectively protect anonymity of continuous queries in location-based service （LBS）. A continuous query issuing model aimed at the problem was proposed. The model incorporated a query issuing interval model and a consecutive queries relationship model. An attacking algorithm aimed at the k-anonymization algorithm was presented based on the model. The algorithm associated a series of snapshots related to continuous queries in order to calculate the probability of each user in the anonymity-set. Then the true query sender was identified by choosing the user with the highest probability. K-anonymized queries were re-identified with different continuity arguments and cardinalities of anonymityset. Experiments demonstrate that the algorithm has high success rate （85%）in identifying query senders when the continuous queries have strong relationship, which is 1. 5 times higher than the success rate without the attacking algorithm and severely undermines the anonymity of the queries.