Differentially Private Unknown Worker Recruitment for Mobile Crowdsensing Using Multi-Armed Bandits

Mobile crowdsensing is a new paradigm by which a platform can recruit mobile workers to perform some sensing tasks by using their smart mobile devices. In this paper, we focus on a privacy-preserving unknown worker recruitment issue. The platform needs to recruit some workers without knowing the qualities of them completing tasks. Meanwhile, these quality information also needs to be protected from disclosure. To tackle these challenges, we model the unknown worker recruitment as a Differentially Private Multi-Armed Bandit (DP-MAB) game by seeing each worker as an arm of DP-MAB and the task completion quality contributed by each worker as the reward of pulling arm. Then, recruiting workers is equivalent to designing a bandit policy of pulling DP-MAB arms. Under this model, we propose a Differentially Private epsilon-First-based arm-pulling (DPF) algorithm and a Differentially Private UCB-based arm-pulling (DPU) algorithm, which can achieve the nearly optimal expected accumulative rewards under a given budget. We also analyze the regrets of the DPF and DPU algorithms and prove that both of them are delta-differentially private on the task completion qualities (delta > 0). Finally, we conduct extensive simulations to verify the significant performances of DPF and DPU based on both the real-trace and synthetic datasets.

Automated summary

This paper focuses on the privacy-preserving recruitment of unknown workers in mobile crowdsensing, proposing algorithms based on differentially private multi-armed bandit games to achieve near-optimal expected accumulative rewards within a given budget. Extensive simulations validate the significant performances of the proposed algorithms based on both real-trace and synthetic datasets.