Privacy-Utility Balanced Cooperative Online Matching in Spatial Crowdsourcing

With the rapid development of spatial temporal crowdsourcing applications, the online task assignment problem has been widely studied as one of the most typical problems. It ensures efficient and accurate matching between tasks and workers. Traditional task assignment only focuses on solving the task assignment on a single platform. Recently, with the widespread application of data sharing technology, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods do not consider data privacy protection during the cooperation process, resulting in the leakage of sensitive information such as users’ location and historical data of platform. In this paper, we propose Privacy-preserving Cooperative Online Matching problem, which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies Differential Privacy. We then propose two privacy-preserving algorithms to solve PCOM. Furthermore, to reduce the impact of location perturbation on matching results, we design a new geographical location perturbation mechanism and a cooperative platform selection algorithm. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.