Constructing dummy query sequences to protect location privacy and query privacy in location-based services
Constructing dummy query sequences to protect location privacy and query privacy in location-based services
复制标题
构建虚拟查询序列以保护基于位置的服务中的位置隐私和查询隐私
DOI：
10.1007/s11280-020-00830-x
复制
发表时间：
2020-07-06
影响因子：
3.7
通讯作者：
Xu, Guandong
中科院分区：
文献类型：
--
作者：
Wu, Zongda;Li, Guiling;Xu, Guandong
基于位置的服务（LBS）已经成为人们日常生活的重要组成部分。然而，LBS在为移动用户提供极大便利的同时，也带来了严重的个人隐私问题，即位置隐私和查询隐私。然而，现有的LBS隐私保护方法一般只考虑位置隐私或查询隐私，没有考虑同时保护这两者的问题。本文提出构建一组虚拟查询序列，以掩盖移动用户的查询位置和查询属性，从而在LBS中保护用户的隐私。首先，我们提出了一种基于客户端的LBS用户隐私保护框架，该框架不仅不需要改变现有的服务器端LBS算法，而且不会影响LBS查询的准确性。其次，在此框架的基础上，引入隐私模型来确定理想虚拟查询序列应满足的约束条件：(1)特征分布的相似性，衡量虚拟查询序列隐藏真实用户查询序列的有效性；(2)用户隐私暴露度，衡量虚拟查询序列掩盖移动用户位置隐私和查询隐私的有效性。最后，我们提出了一种很好地满足隐私模型的实现算法。理论分析和实验验证了该方法的有效性，表明该方法生成的虚拟查询可以有效地保护LBS查询背后的位置隐私和属性隐私。
Location-based services (LBS) have become an important part of people's daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users' privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.