DeePGA: A Privacy-Preserving Data Aggregation Game in Crowdsensing via Deep Reinforcement Learning

The Internet of Things has such a profound impact that we have witnessed crowdsensing has emerged as the most popular sensing paradigm where participants sense and aggregate data to the platform by smart devices. However, the participants may not be willing to involve in data sensing and aggregation if they are not sufficiently compensated or their personalized private information are disclosed. In order to overcome the above issues, this article proposes a payment-privacy protection level (PPL) game, where each participant submits his sensing data with a specified PPL while the platform chooses a corresponding payment to the participant. Additionally, we derive the Nash equilibrium point of the game. Considering that the payment-PPL model is unknown in practice, we employ a reinforcement learning technique, i.e., $Q$ -learning to obtain the payment-PPL strategy in a dynamic payment-PPL game. We further use the deep Q network (DQN), which combines a deep-learning technique with $Q$ -learning to accelerate the learning speed. Through extensive simulations, we verify that our proposed algorithm using DQN achieves superior performance in terms of utilities of both platform and participants and data aggregation accuracy compared with the one using $Q$ -learning.