Cloud-Based Privacy-Preserving Parking Navigation Through Vehicular Communications

Finding a vacant parking space in a congested area, such as shopping mall, airport, etc., is always time-consuming and frustrating for drivers. Real-time parking information can avoid vehicles being cruising on the roads. However, when the drivers are acquiring parking information, their privacy is inevitable to be disclosed. In this paper, to minimize drivers’ hassle and preserve drivers’ privacy, we propose CPARN, a Cloud-based Privacy-preserving pARking Navigation system through vehicular communications, in which a cloud server guides drivers to vacant parking spaces close to their desired destinations without exposing the privacy of drivers, including drivers’ identities, references and routes. Specifically, CPARN allows drivers to query vacant parking spaces in an anonymous manner to a cloud server that maintains the parking information, and retrieve the protected navigation responses from the roadside units when the vehicles are passing through. CPARN has the advantage that it is unnecessary for a vehicle to keep connected with the queried roadside unit to ensure the retrievability of the navigation result, such that the navigation retrieving probability can be significantly improved. Performance evaluation through extensive simulations demonstrates the efficiency and practicality of CPARN.