The new paradigm of fog computing was extended from conventional cloud computing to provide computing and storage capabilities at the edge of the network. Applied to vehicular networks, fog-enabled vehicular computing is expected to become a core feature that can accelerate a multitude of services including crowd-sensing. Accordingly, the security and privacy of vehicles joining the crowd-sensing system have become important issues for cyber defense and smart policing. In addition, to satisfy the demand of crowd-sensing data users, fine-grained access control is required. In this paper, we propose a secure and privacy-preserving crowd-sensing scheme for fog-enabled vehicular computing. The proposed architecture is made by a double layer of fog nodes that is used to generate crowd-sensing tasks for vehicles, then collect, aggregate and analyze the data based on user specifications. To ensure data confidentiality and fined-grained access control, we make use of ciphertext-policy attribute-based encryption with access update policy (CP-ABE-UP), which is a well-known one-to-many encryption technique. The policy update algorithm allows the fog nodes to outsource the crowd-sensing data to other fog nodes or to data users directly. We also adopted the ID-based signature tied to pseudonymous techniques to guarantee the authentication and privacy-preservation of the entities in the system. From the upper fog layer to the data user, we show that an information-centric networking (ICN) approach can be applied to maximize the network resources and enhance the security by avoiding unauthorized and unauthenticated data owners. The security analysis confirms that our approach is secure against known attacks, whereas the simulation results show its efficiency in terms of communication with little computational overhead.

雾计算的新范例已从传统的云计算扩展到在网络边缘提供计算和存储功能。启用雾的车辆计算将应用于车辆网络，它将成为可加速包括人群感知在内的多种服务的核心功能。因此，加入人群感应系统的车辆的安全性和隐私性已成为网络防御和智能警务的重要问题。另外，为了满足人群感知数据用户的需求，需要细粒度的访问控制。在本文中，我们提出了一种用于雾计算的车辆计算的安全且隐私保护的人群感知方案。所提出的架构是由双层雾节点构成的，该雾节点用于生成车辆的人群感知任务，然后收集，根据用户规范汇总和分析数据。为了确保数据的机密性和细粒度的访问控制，我们将基于密文策略属性的加密与访问更新策略（CP-ABE-UP）结合使用，这是一种众所周知的一对多加密技术。策略更新算法允许雾节点将人群感知数据外包给其他雾节点或直接外包给数据用户。我们还采用了与假名技术绑定的基于ID的签名，以确保对系统中实体的身份验证和隐私保护。从高层雾层到数据用户，我们证明了可以应用信息中心网络（ICN）方法来最大化网络资源并通过避免未经授权和未经身份验证的数据所有者来增强安全性。