一种针对LBS中k-匿名连续查询的攻击算法

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.

Attacking algorithm against k-anonymized continuous queries in location based service

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 anonymity-set. 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.