Anonymous Authentication of Visitors for Mobile Crowd Sensing at Amusement Parks

In this paper we focus on authentication and privacy aspects of an application scenario that utilizes mobile crowd sensing for the benefit of amusement park operators and their visitors. The scenario involves a mobile app that gathers visitors’ demographic details, preferences, and current location coordinates, and sends them to the park’s sever for various analyses. These analyses assist the park operators to efficiently deploy their resources, estimate waiting times and queue lengths, and understand the behavior of individual visitors and groups. The app server also offers visitors optimal recommendations on routes and attractions for an improved dynamic experience and minimized wait times. We propose a practical usable solution we call an anonymous authentication of visitors protocol that protects the privacy of visitors even while collecting their details, preferences and location coordinates; deters adversaries outside the park from sending in huge amounts of false data, which lead to erroneous analyses and recommendations and bring down the app server. We utilize queuing theory to analyze the performance of a typical app server receiving numerous simultaneous requests from visitors to process a core function of our protocol.