Privacy Enabled Noise Free Data Collection in Vehicular Networks

Abstract

Many networked users through their devices are interested in participating in distributed sensing and data collection for the purpose of betterment of human society or for earning rewards. Preservation of their location privacy is an important requirement for users participating and contributing to the data collection. We develop a novel privacy preserving approach for collecting noise-free data from vehicular users. Collection of noise-free, "pure" data, enhances its utility in the applications that use it. Location privacy must be preserved from the entity that we call a central controller, that collects all the vehicular data, and is assumed to be adversarial. We collect the data in a noise-free form by introducing temporal and spatial variations using Random Delays and Indirections. We run simulations using network and vehicle simulators driven by a real-world traffic scenario from the city of Luxembourg to evaluate our approach. Our simulation results show that the adversary cannot localize the uploaders within the thresholds of the number of streets and the length of the region of interest chosen by them.