Stable Task Assignment with Range Partition under Differential Privacy

With the development of cloud computing, spatial crowdsourcing (SC) has become a significant concern in data processing, including food delivery and online car-hailing. However, privacy leakage presents a challenge for requesters who need to share their task information with the server. Differential privacy (DP) is a robust privacy protection paradigm that allows the release of useful information while safeguarding requesters’ privacy. However, task assignment under DP often results in ineffective utility. In this paper, we propose a stable task assignment scheme that enables requesters to apply for workers and achieve effective stable matching while preserving the privacy of the requests (tasks). Specifically, we introduce an approach called ECM that achieves stable matching while protecting the preference of requesters. We demonstrate the efficiency and effectiveness of our ECM on synthetic and real datasets.