On the privacy of crowd-sourced data collection for distance-to-empty prediction and eco-routing
Chien-Ming Tseng, Chi-Kin Chau · 2016
The paradigm of crowd-sourced data collection (also known as participatory sensing) has been bolstered by the extensive availability of on-board sensors and electronic devices in nowadays vehicles, which can be applied in a wide range of transportation applications. Distance-to-empty (DTE) is the distance an electric vehicle (EV) or internal-combustion engine (ICE) vehicle can reach before its battery/fuel is exhausted, which is determined by a variety of uncertain factors, such as driving behavior, terrain, types of road, traffic, and vehicle specification. Eco-routing aims to optimize the route selection with lower energy consumption. The accuracy of DTE prediction and eco-routing can be enhanced substantially by the crowd-sourced data collected from diverse drivers and vehicles. However, a critical concomitant issue for crowd-sourced data collection is privacy, because the personal travel history may be misused without consents from the contributing users. To encourage large-scale adoption and contributions of crowd-sourced data collection from end users, this paper addresses the issue of privacy and proposes possible solutions to tackle the challenges. In particular, we discuss a solution of matrix factorization from collaborative filtering to enhance the privacy of crowd-sourced data collection in the context of transportation applications, such as DTE prediction and eco-routing.