Efficient and Privacy-Preserving Distribution Statistics Analytics on Mobile Spatial Data

2026-05-25

AI Summary

This work addresses the challenge of privacy-preserving distributed statistical analysis over massive spatial data in mobile scenarios. The authors propose two efficient schemes, eSpat-B and eSpat+, which leverage octree- and k-d tree-based spatial partitioning, respectively, combined with an enhanced distributed point function (DPF) and an incremental update mechanism. Operating under a two-server non-colluding model, these schemes achieve high-accuracy statistical analysis with significantly reduced computational and communication overhead—by 1.2× and 20×, respectively—while preserving 100% statistical accuracy. To the best of the authors’ knowledge, this is the first system tailored for efficient privacy-preserving distributed statistics on dynamic spatial data from mobile environments, and its security is rigorously proven under standard cryptographic assumptions.

Abstract

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.

Problem

• privacy-preserving
• distribution statistics
• mobile spatial data
• efficient analytics
• data privacy

Innovation

• privacy-preserving analytics
• distributed point functions
• spatial data partitioning
• mobile spatial data
• efficient update algorithm