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. Theoretical analysis and experimental results on real-world mobile trajectory datasets demonstrate that our proposed schemes achieve a reduction of approximately 1.2* in computation overhead, 20* in communication overhead, and maintain 100% accuracy.

翻译：随着移动计算技术的快速发展，智能手机、联网车辆和无人机等各类移动终端与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，移动环境的资源受限和动态特性加剧了隐私挑战：集中处理敏感数据进行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开销或精度损失。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统计分析系统。首先，我们提出eSpat-B方案，该方案利用两个非共谋服务器与新型改进分布式点函数（基于八叉树划分）。进一步考虑空间数据的频繁更新特性，我们提出另一种更高效的方案eSpat+，其核心思想是利用K维树进行空间划分，结合增量式分布式点函数执行统计分析，并设计高效的更新算法。安全性分析表明，本方案在统计全过程中有效保护数据隐私。理论分析与真实移动轨迹数据集实验结果表明，所提方案在计算开销降低约1.2倍、通信开销降低约20倍的同时保持100%准确率。