Thanks to the popularity of mobile devices numerous location-based services (LBS) have emerged. While several privacy-preserving solutions for LBS have been proposed, most of these solutions do not consider the fact that LBS are typically cloud-based nowadays. Outsourcing data and computation to the cloud raises a number of significant challenges related to data confidentiality, user identity and query privacy, fine-grained access control, and query expressiveness. In this work, we propose a privacy-preserving framework for outsourcing LBS to the cloud. The framework supports multi-location queries with fine-grained access control, and search by location attributes, while providing semantic security. In particular, the framework implements a new model that allows the user to govern the trade-off between precision and privacy on a dynamic per-query basis. We also provide a security analysis to show that the proposed scheme preserves privacy in the presence of different threats. We also show the viability of our proposed solution and scalability with the number of locations through an experimental evaluation, using a real-life OpenStreetMap dataset.

中文翻译：

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用于将基于位置的服务外包到云的隐私保护框架

由于移动设备的普及，出现了大量基于位置的服务 (LBS)。虽然已经提出了几种 LBS 隐私保护解决方案，但这些解决方案中的大多数都没有考虑到 LBS 现在通常是基于云的事实。将数据和计算外包给云带来了许多与数据机密性、用户身份和查询隐私、细粒度访问控制和查询表达性相关的重大挑战。在这项工作中，我们提出了一个将 LBS 外包到云端的隐私保护框架。该框架支持具有细粒度访问控制的多位置查询，并通过位置属性进行搜索，同时提供语义安全性。特别是，该框架实现了一个新模型，允许用户在动态的每个查询的基础上管理精度和隐私之间的权衡。我们还提供了一个安全分析，以表明所提出的方案在存在不同威胁的情况下保护了隐私。我们还使用现实生活中的 OpenStreetMap 数据集通过实验评估展示了我们提出的解决方案的可行性和可扩展性以及位置数量。