Monitoring location updates from mobile users has important applications in many areas, ranging from public health (e.g., COVID-19 contact tracing) and national security to social networks and advertising. However, sensitive information can be derived from movement patterns, thus protecting the privacy of mobile users is a major concern. Users may only be willing to disclose their locations when some condition is met, for instance in proximity of a disaster area or an event of interest. Currently, such functionality can be achieved using searchable encryption. Such cryptographic primitives provide provable guarantees for privacy, and allow decryption only when the location satisfies some predicate. Nevertheless, they rely on expensive pairing-based cryptography (PBC), of which direct application to the domain of location updates leads to impractical solutions. We propose secure and efficient techniques for private processing of location updates that complement the use of PBC and lead to significant gains in performance by reducing the amount of required pairing operations. We implement two optimizations that further improve performance: materialization of results to expensive mathematical operations, and parallelization. We also propose an heuristic that brings down the computational overhead through enlarging an alert zone by a small factor (given as system parameter), therefore trading off a small and controlled amount of privacy for significant performance gains. Extensive experimental results show that the proposed techniques significantly improve performance compared to the baseline, and reduce the searchable encryption overhead to a level that is practical in a computing environment with reasonable resources, such as the cloud.

监控来自移动用户的位置更新在许多领域都有重要的应用，从公共卫生（例如，COVID-19 接触者追踪）和国家安全到社交网络和广告。然而，敏感信息可以从运动模式中获得，因此保护移动用户的隐私是一个主要问题。用户可能仅在满足某些条件时才愿意披露他们的位置，例如靠近灾区或感兴趣的事件。目前，此类功能可以使用可搜索加密来实现。这种密码原语为隐私提供了可证明的保证，并且仅在位置满足某些谓词时才允许解密。然而，它们依赖于昂贵的基于配对的密码学 (PBC)，其中直接应用于位置更新领域会导致不切实际的解决方案。我们提出了用于位置更新的私有处理的安全有效的技术，以补充 PBC 的使用，并通过减少所需的配对操作量来显着提高性能。我们实施了两项优化以进一步提高性能：将结果物化为昂贵的数学运算，以及并行化。我们还提出了一种启发式方法，该方法通过将警报区域扩大一个小因子（作为系统参数给出）来降低计算开销，从而牺牲少量可控的隐私来获得显着的性能提升。广泛的实验结果表明，与基线相比，所提出的技术显着提高了性能，