Efficient and Accurate Privacy-Preserving Task Allocation for Unmanned Aerial Vehicle-Based Mobile Crowdsensing

Bowen Zhao, Siyuan Guan, Minghui Chen, Jiali Wu, Cheng Qiao, Yang Xiao

Abstract: Unmanned aerial vehicle-based mobile crowdsensing (UAV-MCS) has emerged as a promising paradigm for large-scale data collection in low-altitude environments. Efficient task allocation is critical in UAV-MCS to reduce the flight distance of UAVs and ensure timely data acquisition. However, since UAVs typically depart from the locations of their operators, task allocation based on geographical coordinates can leak the location privacy of both UAV operators (called workers for simplicity) and task requesters. Existing privacy-preserving task allocation schemes often focus solely on protecting the privacy of UAV operators, while neglecting that of task requesters, and many fail to achieve accurate allocation results, particularly in multi-task scenarios. To address these challenges, we propose an efficient and accurate privacy-preserving task allocation scheme for UAV-MCS that leverages additive secret sharing to simultaneously protect the locations of both UAV operators and task requesters. To enable accurate allocation, we design a privacy-preserving comparison protocol (PAC) based on additive secret sharing and an optimized Paillier cryptosystem. Moreover, to support multi-task allocation under privacy constraints, we develop a privacy-preserving version of the Hungarian method. Experimental results on both real-world and synthetic datasets demonstrate that our scheme effectively reduces UAV travel distance while preserving location privacy, outperforming existing schemes in both efficiency and accuracy.

External IDs:doi:10.1109/tnse.2025.3641172