Reward-based spatial crowdsourcing with differential privacy preservation

In recent years, the popularity of mobile devices has transformed spatial crowdsourcing (SC) into a novel mode for performing complicated projects. Workers can perform tasks at specified locations in return for rewards offered by employers. Existing methods ensure the efficiency of their systems by submitting the workers’ exact locations to a centralised server for task assignment, which can lead to privacy violations. Thus, implementing crowsourcing applications while preserving the privacy of workers’ location is a key issue that needs to be tackled. We propose a reward-based SC method that achieves acceptable utility as measured by task assignment success rates, while efficiently preserving privacy. A differential privacy model ensures rigorous privacy guarantee, and Laplace noise is introduced to protect workers’ exact locations. We then present a reward allocation mechanism that adjusts each piece of the reward for a task using the distribution of the workers’ locations. Through experimental results, we demonstrate that this optimised-reward method is efficient for SC applications.