Privacy-Preserving Multitask Online Matching in Mobile Crowdsensing: A Snapshot-Based Approach

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms.