In the last decade, location information became easily obtainable using off-the-shelf mobile devices. This gave a momentum to developing Location Based Services (LBSs) such as location proximity detection, which can be used to find friends or taxis nearby. LBSs can, however, be easily misused to track users, which draws attention to the need of protecting privacy of these users. In this work, we address this issue by designing, implementing, and evaluating multiple algorithms for Privacy-Preserving Location Proximity (PPLP) that are based on different secure computation protocols. Our PPLP protocols support both circle and polygon range queries and have runtimes from a few to some hundreds of milliseconds and bandwidth requirements from a few hundreds of bytes to one megabyte. Consequently, they are well-suited for different scenarios and offer faster runtimes and savings in bandwidth and computational power as well as security improvements compared to previous PPLP schemes. In addition, the computationally most expensive parts of the PPLP computation can be precomputed in our protocols, such that the input-dependent online phase runs in just a few milliseconds.

中文翻译：

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圆形和多边形的更快的隐私保护位置邻近方案

在过去的十年中，可以使用现成的移动设备轻松获得位置信息。这推动了基于位置的服务（LBS）的发展，例如位置邻近检测，可用于在附近找到朋友或出租车。但是，LBS容易被误用于跟踪用户，这引起了人们对保护这些用户隐私的需求的关注。在这项工作中，我们通过设计，实现和评估基于不同安全计算协议的隐私保护位置邻近（PPLP）的多种算法来解决此问题。我们的PPLP协议支持圆形和多边形范围查询，运行时间从几毫秒到几百毫秒不等，带宽要求从几百个字节到一兆字节。所以，与以前的PPLP方案相比，它们非常适合不同的情况，并提供了更快的运行时间并节省了带宽和计算能力，并提高了安全性。此外，PPLP计算中计算上最昂贵的部分可以在我们的协议中进行预先计算，因此依赖于输入的在线阶段只需几毫秒即可运行。