RAP: A road network-aware multi-location task assignment framework with personalized privacy-preserving in spatial crowdsourcing

Spatial Crowdsourcing (SC) has gained significant attention due to its scalability, low cost, and wide coverage. However, its reliance on accurate worker location information raises privacy concerns. Most existing research assumes uniform privacy protection and focuses on single-location tasks, overlooking multi-location assignments and the need for personalized privacy. In addition, these works assume a two-dimensional Euclidean workspace, which fails to account for the topological constraints and complexities of real-world road networks. To address these challenges, we propose a Road network-aware multi-location task Assignment framework with Personalized privacy-preserving in spatial crowdsourcing (RAP). Specifically, RAP achieves personalized Geo-Graph Indistinguishability (Geo-GI) by applying the Graph Exponential Mechanism (GEM) within localized subgraphs of the road network. This novel design both reduces computational overhead and supports tunable privacy levels, making GEM practical for large-scale deployment. Subsequently, an R-tree-based index is employed to efficiently prune the search space and retrieve candidate workers. Finally, an assignment algorithm matches tasks to the most suitable workers by evaluating the expected travel distance under location uncertainty. Experimental results demonstrate that RAP provides an effective and efficient solution for privacy-preserving multi-location task assignment in SC environments.