Privacy-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection.