Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server
This addresses privacy concerns for workers and tasks in spatial crowdsourcing systems with an untrusted server, representing an incremental improvement over prior methods.
The paper tackles the problem of privacy-preserving task assignment in spatial crowdsourcing by proposing a batch-based approach using Geo-Indistinguishability and Homomorphic Encryption, which improves the number of successfully assigned tasks by 5.9X over batch baselines and 1.74X over existing online solutions.
In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak.