中文摘要：

随着无线技术和移动定位技术的蓬勃发展,出现了一种新的研究领域——基于位置的服务（location-based service,LBS）。用户在享受此类服务的时候不得不把自己的精确位置发送给服务提供商,使得用户可能面临位置隐私泄露的危险。位置k-匿名是最常见的位置隐私保护技术之一,通过将用户的精确位置泛化为一个具有k-匿名性质的区域来达到隐私保护的目的。但是在移动用户连续不断发出位置服务请求的场景下,攻击者能够根据用户的历史请求之间的关系推测出用户的隐私。此种状况下,传统的孤立查询的k-匿名模型失效。文章提出了一种更加优化的k-匿名模型,在满足用户指定匿名度的前提下,利用活动区域内用户的历史位置分布情况寻找出现次数最多且位置分布最密集的k-1个用户组成共同匿名集。实验结果表明,该方法在保证用户要求匿名度的前提下能够有效降低共同匿名区域的面积。

英文摘要：

With the vigorous development of the wireless technology and mobile localization technology, a new research field, location-based service （LBS）, is opened up. When users are enjoying this kind of service, they will have to send their precise position information to service providers. In other words, they may face the risk of location privacies let out. Location k- anonymity is one of the most common location privacy protection technologies, which achieves the purpose of privacy protection by generalizing the user＇s precise position information to be an area with k-anonymity nature. But when the moving user keeps delivering queries of location-based service, the attacker can infer the user＇s privacy information according to the user＇s history requests. Thus the traditional isolated k- anonymity model is failed. On the premise of meeting the user＇s prescribed anonymous degree requirement, this paper puts forward an optimized k-anonymity model, which can use the user＇s historical position information in the active region to look for k-1 users who appear most frequently and have the densest position distribution to constitute the common anonymities set. The experimental result shows that the method can effectively reduce the area of the common anonymous region on the premise of guaranteeing the user＇s prescribed anonymous degree.