Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

5-2013

Abstract

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.

Keywords

Mobile crowd sensing, Amusement park, Anonymous authentication, False data, Partially blind signature scheme

Discipline

Artificial Intelligence and Robotics | Information Security | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Information Security and Trust; Intelligent Systems and Decision Analytics

Publication

Information Security Practice and Experience: 9th International Conference, ISPEC 2013, Lanzhou, China, May 12-14: Proceedings

Volume

7863

First Page

174

Last Page

188

ISBN

9783642380334

Identifier

10.1007/978-3-642-38033-4_13

Publisher

Springer Verlag

City or Country

Berlin

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1007/978-3-642-38033-4_13