The protection of vehicle trajectory in Vehicular ad hoc network is facing many challenges. Among these challenges, one of the most critical issues is to keep the balance between geographical location protection and semantic location protection. Traditional trajectory protection schemes either only focus on geographical location protection or only semantic location protection. Moreover, when trajectory privacy protection is carried out, each location is often given the same protection. This may lead to sensitive locations under insufficient protection and unimportant locations under overprotection. In this paper, based on differential privacy, we propose an optimized privacy differential privacy scheme with reinforcement learning in vehicular ad hoc network. The proposed scheme can dynamically optimize the privacy budget allocation for each location on the vehicle trajectory to reach a better balance between geolocation obfuscation and semantic security. Experiments results demonstrate that the proposed scheme can reduce the risk of geographical and semantic location leakage, and therefore ensure the balance between the utility and privacy.

车载自组织网络中车辆轨迹的保护面临着许多挑战。在这些挑战中，最关键的问题之一是保持地理位置保护和语义位置保护之间的平衡。传统的轨迹保护方案要么只关注地理位置保护，要么只关注语义位置保护。而且，在进行轨迹隐私保护时，往往对每个位置都给予相同的保护。这可能导致保护不足的敏感位置和过度保护的不重要位置。在本文中，基于差分隐私，我们提出了一种在车载自组织网络中具有强化学习的优化隐私差分隐私方案。所提出的方案可以动态优化车辆轨迹上每个位置的隐私预算分配，以在地理定位混淆和语义安全之间取得更好的平衡。实验结果表明，所提出的方案可以降低地理和语义位置泄漏的风险，从而保证实用性和隐私性之间的平衡。