Efficient and Privacy-Preserving Distribution Statistics Analytics on Mobile Spatial Data
2026-05-25 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors created new methods to help analyze location data collected from mobile devices like smartphones and drones without exposing sensitive information. They introduced two systems, eSpat-B and eSpat+, which use special tree structures and secure functions to efficiently process data while keeping it private. Their work shows that these methods reduce the computing and communication needed, all while keeping the data accurate and secure. This helps make real-time mobile data analysis safer and faster.
mobile computingspatial dataprivacy-preservingdistributed point functionsoctree partitioningK-Dimensional treeincremental updatesdata privacymobile trajectoryreal-time analysis
Authors
Xuhao Ren, Mingyang Zhao, Ruichen Zhang, Liehuang Zhu, Dusit Niyato, Bin Xiao
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.