School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Sensors (Basel). 2022 Aug 17;22(16):6141. doi: 10.3390/s22166141.
As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable convenience to users, but it also poses a significant threat to their privacy. A large number of research works have emerged to protect users' privacy. Dummy-based location privacy protection solutions have been widely adopted for their simplicity and enhanced privacy protection results, but there are few reviews on dummy-based location privacy protection. Or, for existing works, some focus on aspects of cryptography, anonymity, or other comprehensive reviews that do not provide enough reviews on dummy-based privacy protection. In this paper, the authors provide a review of dummy-based location privacy protection techniques for location-based services. More specifically, the connection between the level of privacy protection, the quality of service, and the system overhead is summarized. The difference and connection between various location privacy protection techniques are also described. The dummy-based attack models are presented. Then, the algorithms for dummy location selection are analyzed and evaluated. Finally, we thoroughly evaluate different dummy location selection methods and arrive at a highly useful evaluation result. This result is valuable both to users and researchers who are studying this field.
随着智能设备和移动定位技术的提高，基于位置的服务（LBS）越来越受欢迎。LBS 环境为用户提供了极大的便利，但也对用户的隐私构成了重大威胁。已经出现了大量的研究工作来保护用户的隐私。基于虚拟代理的位置隐私保护解决方案因其简单性和增强的隐私保护效果而被广泛采用，但对基于虚拟代理的位置隐私保护的综述却很少。或者，对于现有的工作，有些侧重于密码学、匿名性或其他综合综述，而没有对基于虚拟代理的隐私保护提供足够的综述。在本文中，作者对基于位置服务的基于虚拟代理的位置隐私保护技术进行了综述。更具体地说，总结了隐私保护级别、服务质量和系统开销之间的关系。还描述了各种位置隐私保护技术之间的差异和联系。提出了基于虚拟代理的攻击模型。然后，分析和评估了虚拟位置选择算法。最后，我们彻底评估了不同的虚拟位置选择方法，并得出了一个非常有用的评估结果。这个结果对研究这个领域的用户和研究人员都很有价值。
