GPU-Accelerated Privacy Aggregation Queries of Crowdsensing Spatio-Temporal Data

Yuxi Li, Jingjing Chen, Dong Ji, Fa Zhu, Xingchi Chen, Jong Hyuk Park

Abstract: Addressing the challenges of query privacy leakage and high verification costs in multisource spatio-temporal data queries within crowdsensing environments, we present CrowdPQ–a novel privacy-preserving aggregation query scheme for spatio-temporal crowdsensing data utilizing GPU acceleration. Our approach leverages a robust two-server secure architecture. At the core of our scheme lies a privacy query protocol based on Function Secret Sharing (FSS), ensuring confidentiality while enabling efficient data aggregation queries. Additionally, we propose a non-interactive lightweight verification protocol utilizing Beaver multiplication triples, significantly minimizing resource consumption from invalid query tokens. We provide a comprehensive formal analysis of CrowdPQ in terms of correctness, security, and complexity. Our prototype, implemented in a two-server setting with GPU support, demonstrates superior performance: GPU acceleration reduces query processing time by $4{\times }$ , server-side communication cost by $25{\times }$ , and client-side communication by up to 88.89% compared with existing methods. These results highlight the potential of GPU-enabled secure processing for large-scale crowdsensing data aggregation tasks, offering an innovative pathway for privacy-focused data handling in complex environments.

External IDs:dblp:journals/tce/LiCJZCP25