With the rapid development of mobile computing technology, massive amounts of spatial data are continuously generated from various mobile terminals and sensing devices, such as smartphones, connected vehicles, and drones. Performing efficient distributed statistical analysis on this data is crucial for real-time mobile computing applications. However, the constrained and dynamic nature of mobile environments exacerbates the privacy challenge: centralizing sensitive data for analysis risks severe privacy leaks, while existing privacy-preserving techniques often introduce excessive overhead or inaccuracies. In this paper, we design, implement, and evaluate the first system that supports efficient and privacy-preserving distribution statistics analysis for mobile spatial data. First, we propose eSpat-B, which leverages two non-colluding servers and a newly designed improved distributed point functions (DPF) with octree partitioning. Furthermore, considering the frequent updates of spatial data, we propose another more efficient scheme, eSpat+. The core idea of this scheme is to utilize a K-Dimensional tree for spatial partitioning, combine it with incremental DPF for performing statistics analysis, and design an efficient update algorithm. Security analysis demonstrates that our schemes effectively protect data privacy throughout the statistical process. Extensive experiments on real-world trajectory datasets demonstrate that the proposed schemes significantly outperform existing approaches, reducing computation overhead by up to 1.2x and communication overhead by up to 20x while maintaining 100% statistical accuracy.

翻译：随着移动计算技术的快速发展，智能手机、联网车辆与无人机等移动终端及传感设备持续产生海量空间数据。对这些数据进行高效的分布式统计分析，对于实时移动计算应用至关重要。然而，移动环境固有的资源受限与动态性特征加剧了隐私保护挑战：将敏感数据集中分析存在严重隐私泄露风险，而现有隐私保护技术往往引入过高的开销或计算误差。本文设计、实现并评估了首个支持移动空间数据高效且保护隐私的分布式统计分析的完整系统。首先，我们提出eSpat-B方案，该方案利用两台非共谋服务器与基于八叉树分割的新型改进分布式点函数（DPF）。此外，针对空间数据频繁更新的特性，我们进一步提出更高效的eSpat+方案。其核心思想在于：采用KD树进行空间分割，结合增量式DPF执行统计分析，并设计高效的更新算法。安全分析表明，我们的方案能在整个统计过程中有效保护数据隐私。基于真实轨迹数据集的大量实验证明，所提方案显著优于现有方法，在保持100%统计精度的同时，将计算开销降低至1.2倍以内，通信开销降低至1/20。