Blind Filtering at Third Parties: An Efficient Privacy-Preserving Framework for Location-Based Services
Blind Filtering at Third Parties: An Efficient Privacy-Preserving Framework for Location-Based Services
复制标题
第三方盲过滤：基于位置的服务的有效隐私保护框架
DOI：
10.1109/tmc.2018.2811481
复制
发表时间：
2018
影响因子：
7.9
通讯作者：
Xiang Yang
中科院分区：
文献类型：
--
作者：
Chen Jing;He Kun;Yuan Quan;Chen Min;Du Ruiying;Xiang Yang
近年来，基于位置的服务（LBS）越来越受欢迎，但保护LBS中的用户隐私仍然具有挑战性。根据是否使用可信第三方（TTP），现有的解决方案可以分为：基于TTP和无TTP。前者依赖TTP来保护用户隐私，会造成单点故障，在现实中不切实际。后者不需要任何TTP，但通常会在查询结果中引入冗余的兴趣点（POI）记录，从而在用户侧产生大量的计算和通信成本，使得它们不适合资源受限的移动设备。在本文中，我们提出了一种新颖的框架来保护用户隐私，同时确保效率。我们的框架还使用冗余 POI 记录来保护 LBS 提供商的隐私，但使用半信任的第三方（称为代理）来过滤掉冗余 POI 记录。为了保护隐私免受代理的影响，我们设计了一种新颖的过滤协议——盲过滤器，以允许代理以盲的方式过滤掉冗余的加密 POI 记录。与现有的解决方案相比，我们的框架不仅能够抵御双重身份攻击，而且会产生较低的通信和计算开销。综合分析和实验表明，我们的框架在移动环境中是安全且高效的。
Location-based service (LBS) has gained increasing popularity recently, but protecting users' privacy in LBS remains challenging. Depending on whether a trusted third party (TTP) is used, existing solutions can be classified into: TTP-based and TTP-free. The former relies on a TTP for user privacy protection, which creates a single-point-failure and is thus impractical in reality. The latter does not require any TTP, but usually introduces redundant point-of-interest (POI) records in query result and thus incurs significant computation and communication costs on the user side, making them unsuitable for resource-constrained mobile devices. In this paper, we propose a novel framework to protect user privacy while ensuring efficiency. Our framework also uses redundant POI records to protect privacy against LBS provider but employs a semi-trusted third party, called proxy, to filter out redundant POI records. To protect privacy against proxy, we design a novel filtering protocol, Blind filter, to allow the proxy to filter out redundant encrypted POI records in a blind way. In comparison with existing solutions, our framework is not only resilient to dual identity attack, but also incurs lower communication and computation overhead. Comprehensive analysis and experiments show that our framework is secure and highly efficient in mobile environments.