论文标题
使用加密排序的服务器端指纹基于室内本地化
Server-side Fingerprint-Based Indoor Localization Using Encrypted Sorting
论文作者
论文摘要
GPS信号是导航的主要起源，在室内环境中不起作用。因此，通过依靠基于指纹的方法，Wi-Fi访问点已开始越来越多地用于建筑物内部的本地化和跟踪。但是，通过这些类型的方法，就出现了有关用户隐私的几个问题。恶意的人可以通过简单地分析其无线信号来确定客户的日常习惯和活动。尽管已经努力将隐私纳入现有的基于指纹的方法中，但它们仅限于他们采用的同构密码方案的特征。在本文中，我们建议通过利用另一种同质算法，即DGK来提高这些方法的性能，即具有独特的加密分类功能，从而将大多数计算推向服务器端。我们开发了一个Android应用程序，并在哥伦比亚大学宿舍内测试了我们的系统。与现有系统相比，结果表明，具有更强大的服务器计算功能，可以在客户端获得更多的动力节省，而DGK可以是可行的选择。
GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on a fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a client's daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy into the existing fingerprint-based approaches, they are limited to the characteristics of the homomorphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.
