Personalized Privacy Preserving for Spatial Crowdsourcing by Reinforcement Learning in VANETs

Spatial crowdsourcing is widely used in various applications in vehicular ad hoc networks (VANETs), such as navigation system, traffic control, and event reporting. However, exposing and disclosing the spatial-temporal features of vehicles in the crowdsourcing services will definitely raise serious privacy issues. Existing unified privacy-preserving strategy for vehicles will cause excessive or insufficient preservation, thus result in relatively low-quality services. To solve these problems, we propose personalized privacy preserving for spatial crowdsourcing by reinforcement learning in VANETs. First, we propose a multifactor-based personalized privacy-preserving model to adjust the vehicles’ privacy-preserving level in the scenario of spatial crowdsourcing. And, we employ reinforcement learning to dynamically adjust the model in different situations. Furthermore, we propose an optimal local differential privacy mechanism to maintain the optimal tradeoff between data privacy and data utility, which can achieve personalized privacy preserving in the task allocation. We conduct extensive simulations with the real-world traffic trajectory dataset T-drive, and use the Q-learning algorithm to dynamically adjust the model. The experiments demonstrate that our scheme can enhance data utility by 78.5% with personalized privacy settings.