Abstract Location data reveals users’ trajectories, yet it is often shared to enable many location-based services (LBS). In this paper, we propose a privacy-preserving geospatial query system with geo-hashing and somewhat homomorphic encryption. We geo-hash locations using space-filling curves for locality-preserving dimension reduction, which allows the users to specify granularity preference of their location and is agnostic to specific maps or precoded location models. Our system features three homomorphic algorithms to compute geospatial queries on encrypted location data and encrypted privacy preferences. Comparing with previous work, one of our algorithms reduces the multiplicative depth of a basic homomorphic computation approach by more than half, which significantly speeds it up. We then present an optimized prototype and experimentally demonstrates its utility in spatial cloaking.

中文翻译：

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地理空间查询的通用位置参考和同态评估

摘要 位置数据揭示了用户的轨迹，但它通常被共享以启用许多基于位置的服务 (LBS)。在本文中，我们提出了一种具有地理散列和某种同态加密的隐私保护地理空间查询系统。我们使用空间填充曲线对位置进行地理散列以进行局部保留降维，这允许用户指定其位置的粒度偏好，并且与特定地图或预编码位置模型无关。我们的系统采用三种同态算法来计算对加密位置数据和加密隐私偏好的地理空间查询。与之前的工作相比，我们的一种算法将基本同态计算方法的乘法深度减少了一半以上，从而显着加快了速度。