A Framework for Tradeoff Between Location Privacy Preservation and Quality of Experience in Location Based Services
Tianyi Feng, Zhixiang Zhang, Wai‐Choong Wong, Sumei Sun, Biplab Sikdar
Abstract
Location-based services find a number of applications in vehicular environments such as navigation, parking, infortainment etc. However, the disclosure of vehicles' location information raises multiple privacy issues. To balance the tradeoff between privacy and utility, this paper proposes a framework to preserve users' location privacy while delivering the desired quality of experience (QoE). The proposed framework allows users to quantify the data utility while accessing location-based services under different privacy levels through the QoE metric. The privacy analysis of the proposed framework is provided under two adversary models. Finally, the effectiveness of the proposed framework is demonstrate using the real-world “Dianping” review dataset.